stock_chart_site/backend/app/pipelines/retrain_weekly.py

"""주간 재학습 + 앙상블 가중치 보정.

일요일 02:00 KST 실행:
  1. 시드 10종목 × horizon (1,3,5) 별로 LGBM 학습 (train_one).
  2. 최근 30일 prediction_outcomes 의 model 별 hit_rate 산출, model_performance 적재.
  3. 같은 30일 윈도우에서 chronos vs lgbm hit_rate 로 ensemble_weights 갱신.
     (방법: w_chronos = clamp(0.1, hr_c / (hr_c + hr_l), 0.9), w_lgbm = 1 - w_chronos.
      hit_rate 데이터가 부족하면 default 0.6/0.4 유지.)

지금은 'ensemble' 모델 단일 종류로 predictions 가 쌓이므로, 가중치 보정은
chronos 단독 시뮬레이션 / lgbm 단독 시뮬레이션 hit_rate 비교가 진정한 방식인데,
Phase 4 단순화: 'ensemble' 의 종합 hit_rate 만 model_performance 에 기록하고
가중치는 default 유지. 진짜 비교는 Phase 7 (chronos 단독 + lgbm 단독 예측을
shadow 로 같이 적재하는 구조) 로 확장.
"""
from __future__ import annotations

import json
import logging
from datetime import date, timedelta
from typing import Any

from sqlalchemy import text

from app.db.connection import get_engine
from app.models.lgbm import train_one
from app.seed.seed_tickers import SEED_TICKERS

logger = logging.getLogger(__name__)

HORIZONS = (1, 3, 5)
WINDOW_DAYS = 30


def retrain_all() -> list[dict[str, Any]]:
    """시드 10종목 × horizons 학습."""
    out: list[dict[str, Any]] = []
    for t in SEED_TICKERS:
        for h in HORIZONS:
            try:
                res = train_one(t.code, h)
            except Exception as exc:  # noqa: BLE001
                logger.exception("train_one failed for %s h=%d", t.code, h)
                res = {"code": t.code, "horizon": h, "status": "failed", "error": str(exc)}
            out.append(res)
    return out


def record_performance(as_of: date) -> list[dict[str, Any]]:
    """최근 WINDOW_DAYS 의 prediction_outcomes 로 (code, model, horizon) 별
    hit_rate / mae 산출, model_performance 에 upsert."""
    eng = get_engine()
    start = as_of - timedelta(days=WINDOW_DAYS)
    summary: list[dict[str, Any]] = []
    with eng.begin() as conn:
        rows = conn.execute(
            text(
                """
                SELECT code, model, horizon,
                       AVG(CASE WHEN direction_hit THEN 1.0 ELSE 0.0 END) AS hit_rate,
                       AVG(abs_error)                                     AS mae,
                       COUNT(*)                                           AS n
                  FROM prediction_outcomes
                 WHERE resolved_at >= :start
                 GROUP BY code, model, horizon
                """
            ),
            {"start": start},
        ).all()
        for code, model, horizon, hr, mae, n in rows:
            conn.execute(
                text(
                    """
                    INSERT INTO model_performance
                      (code, model, window_days, as_of, hit_rate, mae, sample_count)
                    VALUES (:c, :m, :w, :as_of, :hr, :mae, :n)
                    ON CONFLICT (code, model, window_days, as_of) DO UPDATE SET
                      hit_rate = EXCLUDED.hit_rate,
                      mae      = EXCLUDED.mae,
                      sample_count = EXCLUDED.sample_count
                    """
                ),
                {
                    "c": code,
                    "m": model,
                    "w": WINDOW_DAYS,
                    "as_of": as_of,
                    "hr": float(hr) if hr is not None else None,
                    "mae": float(mae) if mae is not None else None,
                    "n": int(n),
                },
            )
            summary.append(
                {
                    "code": code,
                    "model": model,
                    "horizon": horizon,
                    "hit_rate": float(hr) if hr is not None else None,
                    "mae": float(mae) if mae is not None else None,
                    "n": int(n),
                }
            )
    return summary


def run_weekly() -> dict[str, Any]:
    """일요일 02:00 KST 호출 entry-point."""
    from datetime import datetime, timezone

    kst = timezone(timedelta(hours=9))
    as_of = datetime.now(kst).date()
    return {
        "as_of": str(as_of),
        "trained": retrain_all(),
        "performance": record_performance(as_of),
    }


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
    out = run_weekly()
    print(json.dumps(out, ensure_ascii=False, indent=2, default=str))