stock_chart_site/backend/app/models/ensemble.py

"""Chronos + LGBM 앙상블 추론.

  final_price[h]   = w_c * chronos.median[h-1] + w_l * lgbm.predicted_close
  final_q10[h]     = w_c * chronos.q10[h-1]    + w_l * lgbm.predicted_close * 0.97
  final_q90[h]     = w_c * chronos.q90[h-1]    + w_l * lgbm.predicted_close * 1.03

LGBM 은 단일 horizon 의 다음 종가(point) 만 주므로, 그 자체로는 신뢰구간이 없음.
근사로 ±3% band 를 LGBM 의 q10/q90 자리에 사용. Chronos 의 sample 분포가
주된 신뢰구간 정보 (Chronos 우세하면 ci 가 좁아짐).

direction 확률:
- LGBM 분류기에서 prob_up/flat/down (3-class) 그대로
- Chronos 는 next-day return 부호 비율: samples.shift1 / base_close - 1 의 부호
- 둘을 같은 가중치로 평균

LGBM 모델이 없으면 Chronos 단독으로 진행 (cold start).
Chronos 도 실패하면 LGBM 단독으로 진행.
"""
from __future__ import annotations

import logging
from dataclasses import dataclass, field

import numpy as np

from app.models.chronos import ChronosForecast
from app.models.chronos import forecast as chronos_forecast
from app.models.lgbm import LgbmForecast
from app.models.lgbm import predict_one as lgbm_predict
from app.models.weights import load_weights

logger = logging.getLogger(__name__)


@dataclass
class EnsembleStep:
    horizon: int                  # 1..H 거래일 후
    target_idx: int               # chronos median 의 0-based 인덱스 (horizon-1)
    point_close: float
    ci_low: float
    ci_high: float
    prob_up: float
    prob_flat: float
    prob_down: float
    direction: str                # 'up' / 'flat' / 'down'
    expected_return: float


@dataclass
class EnsemblePrediction:
    code: str
    base_close: float
    horizons: list[int]
    steps: list[EnsembleStep]
    sources_used: list[str]
    # shadow 저장용 원본 출력 (predict_one.py 가 ensemble + chronos 단독 + lgbm 단독
    # 3 종을 predictions 에 적재해서 retrain_weekly 가 모델별 hit_rate 비교 가능하게 함).
    chronos_raw: ChronosForecast | None = None
    lgbm_raw: dict[int, LgbmForecast] = field(default_factory=dict)


def _chronos_direction(samples: list[list[float]], base_close: float, horizon: int) -> tuple[float, float, float]:
    """Chronos sample 분포에서 (prob_up, prob_flat, prob_down). ±0.3% flat band."""
    if not samples:
        return 0.33, 0.34, 0.33
    arr = np.array(samples)[:, horizon - 1]  # 해당 step 의 sample 값
    ret = arr / base_close - 1.0
    p_up = float((ret > 0.003).mean())
    p_dn = float((ret < -0.003).mean())
    p_fl = 1.0 - p_up - p_dn
    return p_up, p_fl, p_dn


def predict(code: str, *, horizons: tuple[int, ...] = (1, 3, 5)) -> EnsemblePrediction:
    """한 종목에 대해 horizons 별 앙상블 예측. on-demand 추론용."""
    max_h = max(horizons)

    # Chronos: 종가 시계열 가져와서 max_h 까지 예측.
    from app.models.features import build_features  # local import

    ff = build_features(code, lookback_days=400, horizons=horizons, with_targets=False)
    df = ff.df
    if df.empty:
        raise RuntimeError(f"no OHLCV data for {code}")
    closes = df["close"].astype(float).tolist()
    base_close = float(closes[-1])

    sources_used: list[str] = []
    cf: ChronosForecast | None = None
    try:
        cf = chronos_forecast(closes, horizon=max_h, num_samples=30)
        sources_used.append("chronos")
    except Exception as exc:  # noqa: BLE001
        logger.warning("chronos forecast failed for %s: %s", code, exc)

    steps: list[EnsembleStep] = []
    lgbm_raw: dict[int, LgbmForecast] = {}
    for h in horizons:
        lf: LgbmForecast | None = None
        try:
            lf = lgbm_predict(code, h)
            if lf is not None:
                sources_used.append(f"lgbm_h{h}")
                lgbm_raw[h] = lf
        except Exception as exc:  # noqa: BLE001
            logger.warning("lgbm predict failed for %s h=%d: %s", code, h, exc)

        # 가중치 (DB 없으면 default 0.6/0.4).
        w = load_weights(code, h)
        wc, wl = w.w_chronos, w.w_lgbm
        # 한쪽이 없으면 다른 쪽 전부.
        if cf is None and lf is None:
            raise RuntimeError(f"both chronos & lgbm failed for {code} h={h}")
        if cf is None:
            wc, wl = 0.0, 1.0
        if lf is None:
            wc, wl = 1.0, 0.0

        if cf is not None:
            c_med = cf.median[h - 1]
            c_q10 = cf.q10[h - 1]
            c_q90 = cf.q90[h - 1]
        else:
            c_med = c_q10 = c_q90 = base_close  # not used (wc=0)

        if lf is not None:
            l_close = lf.predicted_close
            l_lo = l_close * 0.97
            l_hi = l_close * 1.03
            l_pu, l_pf, l_pd = lf.prob_up, lf.prob_flat, lf.prob_down
        else:
            l_close = l_lo = l_hi = base_close
            l_pu = l_pf = l_pd = 0.0

        point = wc * c_med + wl * l_close
        lo = wc * c_q10 + wl * l_lo
        hi = wc * c_q90 + wl * l_hi

        if cf is not None:
            cp_up, cp_fl, cp_dn = _chronos_direction(cf.samples, base_close, h)
        else:
            cp_up = cp_fl = cp_dn = 0.0

        # direction prob: source 마다 weights 동일하게 가중평균
        if lf is not None and cf is not None:
            p_up = 0.5 * cp_up + 0.5 * l_pu
            p_fl = 0.5 * cp_fl + 0.5 * l_pf
            p_dn = 0.5 * cp_dn + 0.5 * l_pd
        elif cf is not None:
            p_up, p_fl, p_dn = cp_up, cp_fl, cp_dn
        else:
            p_up, p_fl, p_dn = l_pu, l_pf, l_pd

        # 정규화 (혹시 합이 0 가 아닐 때)
        s = max(p_up + p_fl + p_dn, 1e-9)
        p_up, p_fl, p_dn = p_up / s, p_fl / s, p_dn / s
        dir_lbl = "up" if p_up >= max(p_fl, p_dn) else ("down" if p_dn >= p_fl else "flat")

        steps.append(
            EnsembleStep(
                horizon=h,
                target_idx=h - 1,
                point_close=float(point),
                ci_low=float(lo),
                ci_high=float(hi),
                prob_up=float(p_up),
                prob_flat=float(p_fl),
                prob_down=float(p_dn),
                direction=dir_lbl,
                expected_return=float(point / base_close - 1.0),
            )
        )

    return EnsemblePrediction(
        code=code,
        base_close=base_close,
        horizons=list(horizons),
        steps=steps,
        sources_used=sources_used,
        chronos_raw=cf,
        lgbm_raw=lgbm_raw,
    )