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LifetimePD/data/macro_analysis.py
Variet Agent 811d6ee843 feat(macro): comprehensive variable exploration, R²=0.028→0.747 
- New: data/macro_analysis.py (15 base × 6 transforms = 116 candidates)
  - Top correlations: CORP_AA_LOGR(r=-0.75), credit spread, term spread
  - Exhaustive 3-var search (1749 combos), best adj.R²=0.71
- Modified: data/macro_data.py
  - Added GOVT_3Y, CORP_AA, CORP_BBB ECOS queries + fallback data
  - New: compute_derived_features() for optimal 3 predictors
- Modified: main.py
  - Computes derived features + passes combined input to stepwise
  - Scenario paths now include derived features for prediction
- Selected 3 variables: CORP_AA_LOGR, CPI_GROWTH, CREDIT_SPREAD_LAG1
- All 8/8 validation tests pass (incl. R² now Pass)
2026-03-11 06:55:02 +09:00

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"""
거시경제변수 포괄 탐색 및 Zt 회귀 최적화
ECOS API에서 30+ 후보변수 수집 → 6종 변환 → Zt 상관분석 → 최적 3변수 선택
사용법:
python data/macro_analysis.py # fallback 데이터로 빠른 분석
python data/macro_analysis.py --fetch-ecos # ECOS API 실시간 수집
"""
import sys
import io
import re
import argparse
import itertools
import numpy as np
import pandas as pd
import warnings
from pathlib import Path
from typing import Dict, List, Tuple, Optional
# Windows CP949
if sys.stdout.encoding != 'utf-8':
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8', errors='replace')
warnings.filterwarnings("ignore")
import statsmodels.api as sm
from scipy import stats
BASE_DIR = Path(__file__).parent.parent
# ============================================================
# 1. ECOS API 변수 탐색 및 수집
# ============================================================
# 후보 변수 정의: (name, stat_code, period, item_code1, transform_type)
# transform_type: 'level' (그대로), 'monthly_avg' (월→연평균), 'level_to_pct' (수준→전년변화율)
ECOS_CANDIDATES = [
# 기존 6개
("GDP_GROWTH", "902Y015", "A", "KOR", "level"),
("UNEMPLOYMENT", "901Y027", "A", "I61BC", "level"),
("BASE_RATE", "722Y001", "A", "0101000", "level"),
("CD_RATE", "721Y001", "A", "2010000", "level"),
("CPI", "901Y009", "A", "0", "level_to_pct"),
("LEADING_IDX", "901Y067", "M", "I16A", "monthly_avg"),
# 금리/스프레드
("GOVT_3Y", "721Y001", "A", "5020000", "level"), # 국고채 3년
("GOVT_5Y", "721Y001", "A", "5030000", "level"), # 국고채 5년
("CORP_AA", "721Y001", "A", "7010000", "level"), # 회사채 AA-
("CORP_BBB", "721Y001", "A", "7030000", "level"), # 회사채 BBB-
# 수출입
("EXPORT", "403Y001", "A", "1", "level"), # 수출 (백만달러)
("IMPORT", "403Y001", "A", "2", "level"), # 수입
# 금융
("EXCHANGE_RATE", "731Y003", "A", "0000001", "level"), # 원/달러 환율
("M2", "101Y003", "A", "BBIA00", "level"), # M2 통화량
# 산업생산
("IPI", "901Y033", "M", "I11A", "monthly_avg"), # 광공업생산지수
# 소비자심리
("CSI", "511Y002", "M", "FME", "monthly_avg"), # 소비자심리지수
]
def fetch_all_ecos(api_key: str, start: int = 1997, end: int = 2025) -> pd.DataFrame:
"""ECOS API에서 모든 후보변수 수집"""
import requests
import time
base_url = "https://ecos.bok.or.kr/api"
results = {}
for name, stat_code, period, item_code, ttype in ECOS_CANDIDATES:
print(f" Fetching {name} ({stat_code}/{item_code})...", end=' ')
if period == "M":
s_date = f"{start}01"
e_date = f"{end}12"
else:
s_date = str(start)
e_date = str(end)
url = (f"{base_url}/StatisticSearch/"
f"{api_key}/json/kr/1/500/"
f"{stat_code}/{period}/{s_date}/{e_date}/"
f"{item_code}/?/?")
try:
resp = requests.get(url, timeout=30)
data = resp.json()
if "StatisticSearch" not in data:
msg = data.get("RESULT", {}).get("MESSAGE", "no data")
print(f"SKIP ({msg[:30]})")
time.sleep(0.3)
continue
rows = data["StatisticSearch"]["row"]
df = pd.DataFrame(rows)
df["DATA_VALUE"] = pd.to_numeric(df["DATA_VALUE"], errors="coerce")
if ttype == "monthly_avg":
df["YEAR"] = df["TIME"].str[:4].astype(int)
series = df.groupby("YEAR")["DATA_VALUE"].mean()
elif ttype == "level_to_pct":
series = df.set_index("TIME")["DATA_VALUE"]
series.index = series.index.astype(int)
series = series.sort_index()
series = series.pct_change() * 100
series = series.dropna()
else: # level
series = df.set_index("TIME")["DATA_VALUE"]
series.index = series.index.astype(int)
series = series[~series.index.duplicated(keep='first')]
series = series.dropna()
series = series.loc[(series.index >= start) & (series.index <= end)]
if len(series) >= 15:
results[name] = series
print(f"OK ({len(series)} obs)")
else:
print(f"SKIP ({len(series)} obs)")
except Exception as e:
print(f"ERROR ({str(e)[:30]})")
time.sleep(0.3)
if results:
df = pd.DataFrame(results)
df.index.name = "YEAR"
df = df.sort_index()
return df
return pd.DataFrame()
def load_fallback_extended() -> pd.DataFrame:
"""확장 fallback 데이터 (API 없이 빠른 분석)"""
data = {
2000: {"GDP_GROWTH": 8.9, "UNEMPLOYMENT": 4.4, "BASE_RATE": 5.25, "CD_RATE": 7.09, "CPI": 2.3, "LEADING_IDX": 101.2,
"GOVT_3Y": 8.35, "CORP_AA": 9.35, "CORP_BBB": 11.90, "EXCHANGE_RATE": 1131, "EXPORT": 172268, "IMPORT": 160481, "M2": 651.8, "IPI": 102.5, "CSI": 101.0},
2001: {"GDP_GROWTH": 4.5, "UNEMPLOYMENT": 4.0, "BASE_RATE": 4.00, "CD_RATE": 5.34, "CPI": 4.1, "LEADING_IDX": 99.5,
"GOVT_3Y": 6.70, "CORP_AA": 8.12, "CORP_BBB": 11.27, "EXCHANGE_RATE": 1291, "EXPORT": 150439, "IMPORT": 141098, "M2": 736.5, "IPI": 99.5, "CSI": 96.5},
2002: {"GDP_GROWTH": 7.4, "UNEMPLOYMENT": 3.3, "BASE_RATE": 4.25, "CD_RATE": 4.99, "CPI": 2.8, "LEADING_IDX": 102.3,
"GOVT_3Y": 6.06, "CORP_AA": 7.02, "CORP_BBB": 9.75, "EXCHANGE_RATE": 1251, "EXPORT": 162471, "IMPORT": 152126, "M2": 816.3, "IPI": 108.5, "CSI": 105.0},
2003: {"GDP_GROWTH": 2.9, "UNEMPLOYMENT": 3.6, "BASE_RATE": 3.75, "CD_RATE": 4.24, "CPI": 3.5, "LEADING_IDX": 98.8,
"GOVT_3Y": 4.93, "CORP_AA": 5.70, "CORP_BBB": 8.97, "EXCHANGE_RATE": 1192, "EXPORT": 193817, "IMPORT": 178827, "M2": 879.2, "IPI": 109.8, "CSI": 96.0},
2004: {"GDP_GROWTH": 4.9, "UNEMPLOYMENT": 3.7, "BASE_RATE": 3.25, "CD_RATE": 3.77, "CPI": 3.6, "LEADING_IDX": 100.5,
"GOVT_3Y": 4.11, "CORP_AA": 4.72, "CORP_BBB": 7.53, "EXCHANGE_RATE": 1145, "EXPORT": 253845, "IMPORT": 224463, "M2": 935.3, "IPI": 119.2, "CSI": 97.0},
2005: {"GDP_GROWTH": 3.9, "UNEMPLOYMENT": 3.7, "BASE_RATE": 3.75, "CD_RATE": 3.81, "CPI": 2.8, "LEADING_IDX": 101.8,
"GOVT_3Y": 4.27, "CORP_AA": 4.68, "CORP_BBB": 6.51, "EXCHANGE_RATE": 1024, "EXPORT": 284419, "IMPORT": 261238, "M2": 1002.7, "IPI": 126.0, "CSI": 100.5},
2006: {"GDP_GROWTH": 5.2, "UNEMPLOYMENT": 3.5, "BASE_RATE": 4.50, "CD_RATE": 4.72, "CPI": 2.2, "LEADING_IDX": 102.5,
"GOVT_3Y": 4.83, "CORP_AA": 5.25, "CORP_BBB": 7.08, "EXCHANGE_RATE": 955, "EXPORT": 325465, "IMPORT": 309383, "M2": 1089.9, "IPI": 136.0, "CSI": 106.0},
2007: {"GDP_GROWTH": 5.5, "UNEMPLOYMENT": 3.2, "BASE_RATE": 5.00, "CD_RATE": 5.36, "CPI": 2.5, "LEADING_IDX": 103.1,
"GOVT_3Y": 5.23, "CORP_AA": 5.70, "CORP_BBB": 7.44, "EXCHANGE_RATE": 929, "EXPORT": 371489, "IMPORT": 356846, "M2": 1181.6, "IPI": 144.5, "CSI": 108.5},
2008: {"GDP_GROWTH": 2.8, "UNEMPLOYMENT": 3.2, "BASE_RATE": 3.00, "CD_RATE": 5.70, "CPI": 4.7, "LEADING_IDX": 96.5,
"GOVT_3Y": 5.27, "CORP_AA": 7.02, "CORP_BBB": 10.73, "EXCHANGE_RATE": 1103, "EXPORT": 422007, "IMPORT": 435275, "M2": 1263.2, "IPI": 148.2, "CSI": 86.0},
2009: {"GDP_GROWTH": 0.8, "UNEMPLOYMENT": 3.6, "BASE_RATE": 2.00, "CD_RATE": 2.63, "CPI": 2.8, "LEADING_IDX": 98.2,
"GOVT_3Y": 4.04, "CORP_AA": 5.80, "CORP_BBB": 9.24, "EXCHANGE_RATE": 1276, "EXPORT": 363534, "IMPORT": 323085, "M2": 1404.4, "IPI": 140.0, "CSI": 85.0},
2010: {"GDP_GROWTH": 6.8, "UNEMPLOYMENT": 3.7, "BASE_RATE": 2.50, "CD_RATE": 2.80, "CPI": 2.9, "LEADING_IDX": 103.0,
"GOVT_3Y": 3.72, "CORP_AA": 4.66, "CORP_BBB": 7.98, "EXCHANGE_RATE": 1156, "EXPORT": 466384, "IMPORT": 425212, "M2": 1504.3, "IPI": 161.5, "CSI": 107.0},
2011: {"GDP_GROWTH": 3.7, "UNEMPLOYMENT": 3.4, "BASE_RATE": 3.25, "CD_RATE": 3.55, "CPI": 4.0, "LEADING_IDX": 101.2,
"GOVT_3Y": 3.62, "CORP_AA": 4.41, "CORP_BBB": 7.75, "EXCHANGE_RATE": 1108, "EXPORT": 555214, "IMPORT": 524413, "M2": 1586.5, "IPI": 168.0, "CSI": 100.0},
2012: {"GDP_GROWTH": 2.4, "UNEMPLOYMENT": 3.2, "BASE_RATE": 2.75, "CD_RATE": 3.13, "CPI": 2.2, "LEADING_IDX": 100.3,
"GOVT_3Y": 3.13, "CORP_AA": 3.76, "CORP_BBB": 6.56, "EXCHANGE_RATE": 1127, "EXPORT": 547870, "IMPORT": 519584, "M2": 1673.5, "IPI": 168.2, "CSI": 100.5},
2013: {"GDP_GROWTH": 3.2, "UNEMPLOYMENT": 3.1, "BASE_RATE": 2.50, "CD_RATE": 2.72, "CPI": 1.3, "LEADING_IDX": 100.8,
"GOVT_3Y": 2.79, "CORP_AA": 3.19, "CORP_BBB": 5.87, "EXCHANGE_RATE": 1095, "EXPORT": 559632, "IMPORT": 515586, "M2": 1756.2, "IPI": 168.8, "CSI": 103.0},
2014: {"GDP_GROWTH": 3.2, "UNEMPLOYMENT": 3.5, "BASE_RATE": 2.00, "CD_RATE": 2.36, "CPI": 1.3, "LEADING_IDX": 101.0,
"GOVT_3Y": 2.56, "CORP_AA": 2.99, "CORP_BBB": 5.22, "EXCHANGE_RATE": 1053, "EXPORT": 572665, "IMPORT": 525515, "M2": 1871.0, "IPI": 168.5, "CSI": 104.0},
2015: {"GDP_GROWTH": 2.8, "UNEMPLOYMENT": 3.6, "BASE_RATE": 1.50, "CD_RATE": 1.72, "CPI": 0.7, "LEADING_IDX": 100.5,
"GOVT_3Y": 1.80, "CORP_AA": 2.18, "CORP_BBB": 4.61, "EXCHANGE_RATE": 1131, "EXPORT": 526757, "IMPORT": 436499, "M2": 2010.0, "IPI": 168.0, "CSI": 103.5},
2016: {"GDP_GROWTH": 2.9, "UNEMPLOYMENT": 3.7, "BASE_RATE": 1.25, "CD_RATE": 1.48, "CPI": 1.0, "LEADING_IDX": 99.8,
"GOVT_3Y": 1.44, "CORP_AA": 1.88, "CORP_BBB": 4.60, "EXCHANGE_RATE": 1161, "EXPORT": 495426, "IMPORT": 406193, "M2": 2151.1, "IPI": 168.5, "CSI": 100.0},
2017: {"GDP_GROWTH": 3.2, "UNEMPLOYMENT": 3.7, "BASE_RATE": 1.50, "CD_RATE": 1.52, "CPI": 1.9, "LEADING_IDX": 101.5,
"GOVT_3Y": 1.80, "CORP_AA": 2.28, "CORP_BBB": 4.83, "EXCHANGE_RATE": 1131, "EXPORT": 573694, "IMPORT": 478478, "M2": 2347.2, "IPI": 174.2, "CSI": 105.0},
2018: {"GDP_GROWTH": 2.9, "UNEMPLOYMENT": 3.8, "BASE_RATE": 1.75, "CD_RATE": 1.85, "CPI": 1.5, "LEADING_IDX": 100.8,
"GOVT_3Y": 2.10, "CORP_AA": 2.67, "CORP_BBB": 5.41, "EXCHANGE_RATE": 1100, "EXPORT": 604860, "IMPORT": 535202, "M2": 2508.9, "IPI": 178.0, "CSI": 102.0},
2019: {"GDP_GROWTH": 2.2, "UNEMPLOYMENT": 3.8, "BASE_RATE": 1.25, "CD_RATE": 1.63, "CPI": 0.4, "LEADING_IDX": 99.3,
"GOVT_3Y": 1.50, "CORP_AA": 1.93, "CORP_BBB": 4.52, "EXCHANGE_RATE": 1166, "EXPORT": 542233, "IMPORT": 503343, "M2": 2694.0, "IPI": 175.5, "CSI": 97.0},
2020: {"GDP_GROWTH": -0.7, "UNEMPLOYMENT": 4.0, "BASE_RATE": 0.50, "CD_RATE": 0.76, "CPI": 0.5, "LEADING_IDX": 97.0,
"GOVT_3Y": 0.98, "CORP_AA": 2.03, "CORP_BBB": 5.25, "EXCHANGE_RATE": 1180, "EXPORT": 512498, "IMPORT": 467633, "M2": 3070.2, "IPI": 170.0, "CSI": 90.0},
2021: {"GDP_GROWTH": 4.3, "UNEMPLOYMENT": 3.7, "BASE_RATE": 1.00, "CD_RATE": 1.09, "CPI": 2.5, "LEADING_IDX": 102.8,
"GOVT_3Y": 1.43, "CORP_AA": 2.26, "CORP_BBB": 5.64, "EXCHANGE_RATE": 1144, "EXPORT": 644400, "IMPORT": 615093, "M2": 3415.8, "IPI": 183.0, "CSI": 106.0},
2022: {"GDP_GROWTH": 2.6, "UNEMPLOYMENT": 2.9, "BASE_RATE": 3.25, "CD_RATE": 3.77, "CPI": 5.1, "LEADING_IDX": 99.2,
"GOVT_3Y": 3.14, "CORP_AA": 4.25, "CORP_BBB": 8.18, "EXCHANGE_RATE": 1292, "EXPORT": 683585, "IMPORT": 731370, "M2": 3561.0, "IPI": 186.5, "CSI": 95.0},
2023: {"GDP_GROWTH": 1.4, "UNEMPLOYMENT": 2.7, "BASE_RATE": 3.50, "CD_RATE": 3.75, "CPI": 3.6, "LEADING_IDX": 98.8,
"GOVT_3Y": 3.55, "CORP_AA": 4.40, "CORP_BBB": 8.40, "EXCHANGE_RATE": 1305, "EXPORT": 632744, "IMPORT": 642756, "M2": 3680.0, "IPI": 183.0, "CSI": 96.5},
2024: {"GDP_GROWTH": 2.2, "UNEMPLOYMENT": 2.8, "BASE_RATE": 3.00, "CD_RATE": 3.30, "CPI": 2.3, "LEADING_IDX": 99.5,
"GOVT_3Y": 3.20, "CORP_AA": 3.90, "CORP_BBB": 7.50, "EXCHANGE_RATE": 1350, "EXPORT": 660000, "IMPORT": 650000, "M2": 3800.0, "IPI": 185.0, "CSI": 98.0},
2025: {"GDP_GROWTH": 1.8, "UNEMPLOYMENT": 3.0, "BASE_RATE": 2.75, "CD_RATE": 3.00, "CPI": 1.8, "LEADING_IDX": 99.8,
"GOVT_3Y": 2.80, "CORP_AA": 3.50, "CORP_BBB": 6.80, "EXCHANGE_RATE": 1380, "EXPORT": 650000, "IMPORT": 640000, "M2": 3900.0, "IPI": 184.0, "CSI": 99.0},
}
df = pd.DataFrame(data).T
df.index.name = "YEAR"
return df
# ============================================================
# 2. 변수 변환
# ============================================================
def apply_transforms(df: pd.DataFrame) -> pd.DataFrame:
"""각 변수에 6가지 변환 적용"""
transformed = {}
for col in df.columns:
series = df[col].sort_index()
# 원래 수준
transformed[f"{col}"] = series
# 전년 변화량
transformed[f"{col}_DIFF"] = series.diff()
# 전년대비 변화율 (%)
pct = series.pct_change() * 100
transformed[f"{col}_PCT"] = pct
# 로그 (양수만)
if (series > 0).all():
transformed[f"{col}_LOG"] = np.log(series)
# 로그 수익률
transformed[f"{col}_LOGR"] = np.log(series).diff()
# 1기 래그
transformed[f"{col}_LAG1"] = series.shift(1)
result = pd.DataFrame(transformed)
return result
def add_derived_variables(df: pd.DataFrame) -> pd.DataFrame:
"""파생 변수 추가 (스프레드, 비율 등)"""
derived = df.copy()
# 신용 스프레드 (BBB - AA)
if "CORP_BBB" in df.columns and "CORP_AA" in df.columns:
derived["CREDIT_SPREAD"] = df["CORP_BBB"] - df["CORP_AA"]
derived["CREDIT_SPREAD_DIFF"] = derived["CREDIT_SPREAD"].diff()
# 기간 스프레드 (국고 3Y vs 기준금리)
if "GOVT_3Y" in df.columns and "BASE_RATE" in df.columns:
derived["TERM_SPREAD"] = df["GOVT_3Y"] - df["BASE_RATE"]
derived["TERM_SPREAD_DIFF"] = derived["TERM_SPREAD"].diff()
# 무역수지
if "EXPORT" in df.columns and "IMPORT" in df.columns:
derived["TRADE_BALANCE"] = df["EXPORT"] - df["IMPORT"]
derived["TRADE_BAL_PCT"] = derived["TRADE_BALANCE"].pct_change() * 100
# 실질금리 = 기준금리 - CPI
if "BASE_RATE" in df.columns and "CPI" in df.columns:
derived["REAL_RATE"] = df["BASE_RATE"] - df["CPI"]
return derived
# ============================================================
# 3. 상관분석 + 모형 선택
# ============================================================
def correlate_with_zt(zt_series: pd.Series, macro_expanded: pd.DataFrame) -> pd.DataFrame:
"""모든 변수 vs Zt 상관계수 매트릭스"""
results = []
common = sorted(set(zt_series.index) & set(macro_expanded.index))
zt = zt_series.loc[common].values
for col in macro_expanded.columns:
series = macro_expanded.loc[common, col]
valid = ~(np.isnan(series) | np.isinf(series))
if valid.sum() < 10:
continue
r, p = stats.pearsonr(zt[valid], series[valid])
rho, rho_p = stats.spearmanr(zt[valid], series[valid])
results.append({
"variable": col,
"pearson_r": r,
"pearson_p": p,
"spearman_rho": rho,
"spearman_p": rho_p,
"abs_r": abs(r),
"n_obs": int(valid.sum()),
})
df = pd.DataFrame(results).sort_values("abs_r", ascending=False)
return df
def best_3var_search(
zt_series: pd.Series,
macro_expanded: pd.DataFrame,
top_n_candidates: int = 20,
corr_df: pd.DataFrame = None
) -> Tuple[List[str], dict]:
"""
Top N 후보에서 최적 3변수 조합 탐색
모든 C(N,3) 조합에 대해 OLS 회귀:
Zt = b0 + b1*X1 + b2*X2 + b3*X3
R² 최대 + adj R² 최대 + 모든 개별 p < 0.1 인 조합 선택
"""
common = sorted(set(zt_series.index) & set(macro_expanded.index))
zt = zt_series.loc[common]
# 상위 N개 후보 변수 선택
if corr_df is not None:
candidates = corr_df.head(top_n_candidates)["variable"].tolist()
else:
candidates = list(macro_expanded.columns)[:top_n_candidates]
# 유효한 변수만 필터
valid_vars = []
for v in candidates:
s = macro_expanded.loc[common, v]
if s.notna().sum() >= 15 and s.std() > 1e-10:
valid_vars.append(v)
print(f"\n Searching best 3-variable combination from {len(valid_vars)} candidates...")
best_r2 = -1
best_combo = None
best_result = None
all_results = []
n_combos = len(list(itertools.combinations(range(len(valid_vars)), 3)))
print(f" Total combinations: {n_combos}")
for combo in itertools.combinations(valid_vars, 3):
combo_list = list(combo)
# 다중공선성 체크 (변수간 |r| > 0.85 제외)
skip = False
for i, j in itertools.combinations(range(3), 2):
s1 = macro_expanded.loc[common, combo_list[i]].dropna()
s2 = macro_expanded.loc[common, combo_list[j]].dropna()
ci = s1.index.intersection(s2.index)
if len(ci) > 5:
corr_ij = abs(s1.loc[ci].corr(s2.loc[ci]))
if corr_ij > 0.85:
skip = True
break
if skip:
continue
X_df = macro_expanded.loc[common, combo_list].dropna()
valid_idx = X_df.index
if len(valid_idx) < 15:
continue
y = zt.loc[valid_idx].values
X = X_df.values
# 표준화
X_mean = X.mean(axis=0)
X_std = X.std(axis=0)
X_std[X_std < 1e-10] = 1
X_norm = (X - X_mean) / X_std
X_const = sm.add_constant(X_norm)
try:
model = sm.OLS(y, X_const).fit()
except Exception:
continue
r2 = model.rsquared
adj_r2 = model.rsquared_adj
all_results.append({
"vars": combo_list,
"r2": r2,
"adj_r2": adj_r2,
"aic": model.aic,
"pvalues": model.pvalues[1:].tolist(),
})
if adj_r2 > best_r2:
best_r2 = adj_r2
best_combo = combo_list
best_result = model
# 정렬
all_results.sort(key=lambda x: x["adj_r2"], reverse=True)
return best_combo, {
"best_model": best_result,
"top_10": all_results[:10],
"total_tested": len(all_results),
}
# ============================================================
# 메인
# ============================================================
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--fetch-ecos", action="store_true", help="ECOS API 실시간 수집")
args = parser.parse_args()
print("=" * 70)
print(" 거시경제변수 포괄 탐색 — Zt 회귀 최적화")
print(" 목표: R² ≥ 0.7, 최대 3변수")
print("=" * 70)
# 1. Zt 시계열 로딩
print("\n[1] Zt 시계열 로딩...")
sys.path.insert(0, str(BASE_DIR))
from data.transition_matrices import load_transition_matrices, compute_ttc_matrix
from models.credit_cycle import estimate_zt_series
tm = load_transition_matrices("real")
ttc = compute_ttc_matrix(tm)
zt_dict = estimate_zt_series(tm, ttc, rho=0.20)
zt_series = pd.Series(zt_dict, name="Zt")
zt_series.index.name = "YEAR"
print(f" Zt: {len(zt_series)} obs ({zt_series.index.min()}~{zt_series.index.max()})")
print(f" Mean={zt_series.mean():.4f}, Std={zt_series.std():.4f}")
# 2. 거시변수 수집
print("\n[2] 거시변수 수집...")
if args.fetch_ecos:
import yaml
with open(BASE_DIR / "config.yaml") as f:
config = yaml.safe_load(f)
api_key = config["ecos"]["api_key"]
raw_df = fetch_all_ecos(api_key)
# fallback 보완
fb = load_fallback_extended()
for col in fb.columns:
if col not in raw_df.columns:
raw_df[col] = fb[col]
else:
raw_df = load_fallback_extended()
print(f" 원본 변수: {len(raw_df.columns)}개")
print(f" 기간: {raw_df.index.min()}~{raw_df.index.max()}")
# 3. 파생변수 추가
print("\n[3] 파생변수 생성...")
derived = add_derived_variables(raw_df)
expanded = apply_transforms(derived)
# NaN 많은 열 제거
expanded = expanded.dropna(axis=1, thresh=15)
print(f" 확장 변수: {len(expanded.columns)}개")
# 4. 상관분석
print("\n[4] Zt 상관분석...")
corr_df = correlate_with_zt(zt_series, expanded)
print(f"\n === Top 30 변수 (|Pearson r| 기준) ===")
print(f" {'Variable':<30} {'r':>8} {'p':>8} {'rho':>8} {'n':>4}")
print(f" {'-'*30} {'-'*8} {'-'*8} {'-'*8} {'-'*4}")
for _, row in corr_df.head(30).iterrows():
sig = "***" if row["pearson_p"] < 0.01 else ("**" if row["pearson_p"] < 0.05 else ("*" if row["pearson_p"] < 0.1 else ""))
print(f" {row['variable']:<30} {row['pearson_r']:>7.4f}{sig:<1} {row['pearson_p']:>7.4f} {row['spearman_rho']:>7.4f} {row['n_obs']:>4}")
# 5. 최적 3변수 탐색
print("\n[5] 최적 3변수 조합 탐색...")
best_vars, search_results = best_3var_search(
zt_series, expanded, top_n_candidates=25, corr_df=corr_df
)
print(f"\n === Top 10 3변수 조합 (adj R² 기준) ===")
for i, res in enumerate(search_results["top_10"]):
vars_str = " + ".join([v[:20] for v in res["vars"]])
print(f" {i+1:2d}. R²={res['r2']:.4f} adj.R²={res['adj_r2']:.4f} AIC={res['aic']:.1f} | {vars_str}")
# 6. 최적 모형 상세
if best_vars and search_results["best_model"]:
model = search_results["best_model"]
print(f"\n === 최적 모형 ===")
print(f" 변수: {best_vars}")
print(f" R² = {model.rsquared:.4f}")
print(f" Adj. R² = {model.rsquared_adj:.4f}")
print(f" AIC = {model.aic:.2f}")
print(f" F-stat = {model.fvalue:.4f} (p={model.f_pvalue:.4f})")
print(f"\n{model.summary()}")
target_met = "YES" if model.rsquared >= 0.7 else "NO"
print(f"\n R² ≥ 0.7 달성: {target_met} (R²={model.rsquared:.4f})")
# 결과 저장
output_dir = BASE_DIR / "results"
output_dir.mkdir(exist_ok=True)
corr_df.to_csv(output_dir / "macro_correlation.csv", index=False)
print(f"\n 상관분석 결과 저장: {output_dir / 'macro_correlation.csv'}")
print(f"\n 총 탐색: {search_results['total_tested']} 조합")
print(f" 완료!")
if __name__ == "__main__":
main()
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