"""
모든 봉·캔들 특징 행렬에서 매수/매도 규칙을 탐색합니다 (인과적 백테스트).

  python simulation.py discover
"""

from __future__ import annotations

import json
import random
from dataclasses import asdict, dataclass, field
from pathlib import Path

import numpy as np
import pandas as pd

from candle_features import (
    FEATURE_BOOL_COLS,
    build_master_feature_matrix,
    interval_prefix,
)
from config import (
    BUY_COOLDOWN_SEC,
    BUY_MAX_BB_POS_CHASE,
    DISCOVER_MAX_TRADES,
    DISCOVER_TRADE_PENALTY_PCT,
    DOWNLOAD_INTERVALS,
    ENTRY_INTERVAL,
    SELL_COOLDOWN_SEC,
    SELL_MIN_BB_POS,
    SIGNAL_EDGE_ONLY,
    SIM_INITIAL_CASH_KRW,
    SIM_MIN_ORDER_KRW,
    SYMBOL,
    TRADE_MIN_GAP_BARS,
    TRADING_FEE_RATE,
)
from strategy import (
    SIGNAL_BUY_LOWER,
    SIGNAL_SELL_STOP,
    SIGNAL_SELL_UPPER,
)

RULES_FILE = Path(__file__).parent / "discovered_rules.json"

# 탐색에 쓸 특징 (불리언 컬럼)
SEARCH_FEATURES: tuple[str, ...] = FEATURE_BOOL_COLS

# 상위 봉 과열·하락 차단용 부정 조건 후보
NEG_BLOCK_FEATURES: tuple[str, ...] = (
    "cross_up_upper",
    "above_upper",
    "cross_down_lower",
    "shooting_star",
    "ichi_above_cloud",
    "bb_zone_top",
    "bb_pos_high",
)

# 탐색 규칙 적용 시 항상 매수 차단 (상단 돌파·과열)
BUY_SAFETY_BLOCK: tuple[str, ...] = (
    "m3:above_upper",
    "m3:cross_up_upper",
    "m10:above_upper",
    "m10:cross_up_upper",
)

# 연속 봉에서 오래 참 → 엣지 없으면 과다 체결
LEVEL_STATE_FEATURES: tuple[str, ...] = (
    "below_lower",
    "above_upper",
    "inside_band",
    "bb_zone_bottom",
    "bb_zone_top",
    "bb_pos_low",
    "bb_pos_high",
    "ichi_price_above_tenkan",
    "ichi_price_below_tenkan",
)


@dataclass
class DiscoveredRules:
    """탐색된 매수/매도 규칙 (모든 봉 특징 조합)."""

    name: str = "discovered"
    buy_all: list[str] = field(default_factory=list)
    buy_any: list[list[str]] = field(default_factory=list)
    sell_all: list[str] = field(default_factory=list)
    sell_stop: list[str] = field(default_factory=list)
    train_return_pct: float = 0.0
    test_return_pct: float = 0.0
    full_return_pct: float = 0.0
    trade_count: int = 0


def predicate_column(key: str) -> tuple[str, bool]:
    """
    'm60:cross_up_lower' / 'd1:!above_upper' -> (컬럼명, negated).
    """
    if ":" not in key:
        raise ValueError(f"잘못된 predicate: {key}")
    prefix, rest = key.split(":", 1)
    neg = rest.startswith("!")
    feat = rest[1:] if neg else rest
    return f"{prefix}_{feat}", neg


def _predicate_feature(key: str) -> str:
    """predicate에서 특징명만 추출 (! 제외)."""
    rest = key.split(":", 1)[1]
    return rest[1:] if rest.startswith("!") else rest


def is_level_state_predicate(key: str) -> bool:
    """한번 참이면 여러 봉 연속 참인 상태형 조건."""
    return _predicate_feature(key) in LEVEL_STATE_FEATURES


def is_weak_sell_predicate(key: str) -> bool:
    """
    !cross_* / !below_* 등 — 대부분의 봉에서 참이라 매도가 과다해짐.
    """
    if ":" not in key:
        return False
    rest = key.split(":", 1)[1]
    if not rest.startswith("!"):
        return False
    feat = rest[1:]
    if feat.startswith("cross_"):
        return True
    return feat in ("below_lower", "above_upper", "inside_band")


def is_blocked_buy_predicate(key: str) -> bool:
    """진입(3분) 봉의 상태형 매수 조건은 탐색에서 제외."""
    pfx = interval_prefix(ENTRY_INTERVAL)
    return key.startswith(f"{pfx}:") and is_level_state_predicate(key)


# 고점 추격 매수(상단 구간·과열) — 탐색·체결에서 제외
CHASE_BUY_FEATURES: tuple[str, ...] = (
    "bb_zone_top",
    "bb_zone_high",
    "bb_pos_high",
    "above_upper",
    "cross_up_upper",
)

# 저점·반등 매수 트리거
VALUE_BUY_FEATURES: tuple[str, ...] = (
    "cross_up_lower",
    "bb_zone_bottom",
    "bb_zone_low",
    "hammer",
    "bb_pos_low",
    "ichi_tk_cross_up",
    "cross_down_lower",
)


def is_chase_buy_predicate(key: str) -> bool:
    """밴드 상단·고점 추격 매수 조건."""
    if ":" not in key:
        return False
    rest = key.split(":", 1)[1]
    if rest.startswith("!"):
        return False
    return _predicate_feature(key) in CHASE_BUY_FEATURES


def is_value_buy_predicate(key: str) -> bool:
    """하단 돌파·반등형 매수 조건."""
    if ":" not in key:
        return False
    rest = key.split(":", 1)[1]
    if rest.startswith("!"):
        return False
    return _predicate_feature(key) in VALUE_BUY_FEATURES


def _entry_bb_pos_col() -> str:
    return f"{interval_prefix(ENTRY_INTERVAL)}_bb_pos"


def discover_score(return_pct: float, trade_count: int) -> float:
    """탐색 목적함수: 수익률 − 과다 거래 패널티."""
    excess = max(0, trade_count - DISCOVER_MAX_TRADES)
    return return_pct - excess * DISCOVER_TRADE_PENALTY_PCT


def _rising_edge(mask: np.ndarray, i: int) -> bool:
    """i번째 봉에서 조건이 새로 참이 됐는지."""
    if not bool(mask[i]):
        return False
    if i == 0:
        return True
    return not bool(mask[i - 1])


def _trigger_at(mask: np.ndarray, i: int, edge_only: bool = SIGNAL_EDGE_ONLY) -> bool:
    if edge_only:
        return _rising_edge(mask, i)
    return bool(mask[i])


def _mask_for_keys(matrix: pd.DataFrame, keys: list[str]) -> np.ndarray:
    """AND 조건 마스크."""
    n = len(matrix)
    if not keys:
        return np.ones(n, dtype=bool)
    out = np.ones(n, dtype=bool)
    for key in keys:
        col, neg = predicate_column(key)
        if col not in matrix.columns:
            return np.zeros(n, dtype=bool)
        vals = matrix[col].fillna(0).astype(bool).to_numpy()
        if neg:
            vals = ~vals
        out &= vals
    return out


def _unsafe_buy_mask(matrix: pd.DataFrame) -> np.ndarray:
    """
    고점 매수 차단.

    - 상단/상향돌파
    - 3분 밴드 하단(low)인데 유성형 → 급등 끝물음 (5/27 사례)
    """
    n = len(matrix)
    unsafe = np.zeros(n, dtype=bool)
    for key in BUY_SAFETY_BLOCK:
        col, neg = predicate_column(key)
        if neg or col not in matrix.columns:
            continue
        unsafe |= matrix[col].fillna(0).astype(bool).to_numpy()
    if "Close" in matrix.columns:
        roll_hi = matrix["Close"].astype(float).rolling(20, min_periods=5).max()
        near_peak = matrix["Close"].astype(float) >= roll_hi * 0.97
        if "m3_bb_pos_low" in matrix.columns and "m3_shooting_star" in matrix.columns:
            # 급등 끝 고점: 밴드하단+유성형 (5/27 02:33) — 차트상 매도 구간
            toxic = (
                matrix["m3_bb_pos_low"].fillna(0).astype(bool)
                & matrix["m3_shooting_star"].fillna(0).astype(bool)
                & near_peak.fillna(False)
            )
            unsafe |= toxic.to_numpy()
        if "m30_hammer" in matrix.columns:
            # 30분 망치만으로 고점 매수 (5/27 00:00)
            unsafe |= (
                matrix["m30_hammer"].fillna(0).astype(bool) & near_peak.fillna(False)
            ).to_numpy()
    if _entry_bb_pos_col() in matrix.columns:
        pos = matrix[_entry_bb_pos_col()].fillna(0.5).astype(float).to_numpy()
        unsafe |= pos >= BUY_MAX_BB_POS_CHASE
    for key in CHASE_BUY_FEATURES:
        col = f"{interval_prefix(ENTRY_INTERVAL)}_{key}"
        if col in matrix.columns:
            unsafe |= matrix[col].fillna(0).astype(bool).to_numpy()
    return unsafe


def _value_buy_gate_mask(matrix: pd.DataFrame, group: list[str]) -> np.ndarray:
    """
    매수 그룹별: 저점 트리거(value) 또는 3분 bb_pos < BUY_MAX_BB_POS_CHASE 일 때만 허용.
    """
    n = len(matrix)
    pos_col = _entry_bb_pos_col()
    if pos_col in matrix.columns:
        pos_ok = (
            matrix[pos_col].fillna(0.5).astype(float).to_numpy()
            < BUY_MAX_BB_POS_CHASE
        )
    else:
        pos_ok = np.ones(n, dtype=bool)

    value_keys = [k for k in group if is_value_buy_predicate(k)]
    if not value_keys:
        return pos_ok

    value_hit = _mask_for_keys(matrix, value_keys)
    return pos_ok | value_hit


def _unsafe_sell_mask(matrix: pd.DataFrame) -> np.ndarray:
    """
    저점·반등 구간 매도 차단.

    - 3분 bb_pos < SELL_MIN_BB_POS
    - 망치·밴드 하단 구간에서 상단돌파 익절 방지 (5/26 01:48 유형)
    """
    n = len(matrix)
    blocked = np.zeros(n, dtype=bool)
    pos_col = _entry_bb_pos_col()
    if pos_col in matrix.columns:
        pos = matrix[pos_col].fillna(0.5).astype(float).to_numpy()
        blocked |= pos < SELL_MIN_BB_POS
    pfx = interval_prefix(ENTRY_INTERVAL)
    for feat in ("hammer", "bb_zone_bottom", "bb_zone_low", "bb_pos_low"):
        col = f"{pfx}_{feat}"
        if col in matrix.columns:
            blocked |= matrix[col].fillna(0).astype(bool).to_numpy()
    return blocked


def buy_mask(matrix: pd.DataFrame, rules: DiscoveredRules) -> np.ndarray:
    """
    매수 마스크 = (buy_all) 또는 (buy_any 각 그룹의 AND) 중 하나 + 안전필터.

    buy_any는 추가 분기(OR)이지, buy_all과의 AND가 아닙니다.
    """
    n = len(matrix)
    groups: list[list[str]] = []
    if rules.buy_all:
        groups.append(list(rules.buy_all))
    for g in rules.buy_any:
        if g:
            groups.append(list(g))
    if not groups:
        return np.zeros(n, dtype=bool)
    any_ok = np.zeros(n, dtype=bool)
    for group in groups:
        raw = _mask_for_keys(matrix, group)
        any_ok |= raw & _value_buy_gate_mask(matrix, group)
    return any_ok & ~_unsafe_buy_mask(matrix)


def sell_mask(matrix: pd.DataFrame, rules: DiscoveredRules, stop: bool = False) -> np.ndarray:
    keys = rules.sell_stop if stop else rules.sell_all
    raw = _mask_for_keys(matrix, keys)
    if stop:
        return raw
    return raw & ~_unsafe_sell_mask(matrix)


def sanitize_rules(rules: DiscoveredRules) -> DiscoveredRules:
    """탐색 결과에서 추격 매수·무의미 조건 제거."""
    rules.buy_all = [p for p in rules.buy_all if not is_chase_buy_predicate(p)]
    rules.buy_any = [
        [p for p in g if not is_chase_buy_predicate(p)]
        for g in rules.buy_any
    ]
    rules.buy_any = [g for g in rules.buy_any if g]
    rules.sell_all = [p for p in rules.sell_all if not is_weak_sell_predicate(p)]
    return rules


def _discovery_seed() -> DiscoveredRules:
    """탐색 시드: 하단 돌파 기준선 (combination_seed의 상단 추격 매수 미사용)."""
    return _baseline_rules()


def generate_predicate_pool(intervals: list[int]) -> list[str]:
    """탐색 후보 predicate 목록."""
    pool: list[str] = []
    for iv in intervals:
        pfx = interval_prefix(iv)
        for feat in SEARCH_FEATURES:
            pool.append(f"{pfx}:{feat}")
        if iv != ENTRY_INTERVAL:
            for feat in NEG_BLOCK_FEATURES:
                pool.append(f"{pfx}:!{feat}")
    return pool


def list_rule_signal_edges(
    matrix: pd.DataFrame,
    rules: DiscoveredRules,
) -> list[tuple[pd.Timestamp, str]]:
    """
    전 기간 규칙 엣지 신호(체결 여부와 무관).

    Returns:
        (timestamp, action) — buy_signal | sell_signal | sell_stop_signal
    """
    idx = matrix.index
    b_mask = buy_mask(matrix, rules)
    s_mask = sell_mask(matrix, rules, stop=False)
    stop_mask = (
        sell_mask(matrix, rules, stop=True)
        if rules.sell_stop
        else np.zeros(len(matrix), dtype=bool)
    )
    out: list[tuple[pd.Timestamp, str]] = []
    for i in range(len(matrix)):
        if _rising_edge(b_mask, i):
            out.append((idx[i], "buy_signal"))
        if _rising_edge(s_mask, i):
            out.append((idx[i], "sell_signal"))
        if rules.sell_stop and _rising_edge(stop_mask, i):
            out.append((idx[i], "sell_stop_signal"))
    return out


def generate_trade_events(
    matrix: pd.DataFrame,
    rules: DiscoveredRules,
) -> list[tuple[pd.Timestamp, str, str]]:
    """
    규칙에 따른 체결 이벤트 목록.

    Returns:
        (timestamp, action, signal_name)
    """
    close = matrix["Close"].astype(float).to_numpy()
    idx = matrix.index
    b_mask = buy_mask(matrix, rules)
    s_mask = sell_mask(matrix, rules, stop=False)
    stop_mask = (
        sell_mask(matrix, rules, stop=True)
        if rules.sell_stop
        else np.zeros(len(matrix), dtype=bool)
    )

    events: list[tuple[pd.Timestamp, str, str]] = []
    qty = 0.0
    last_buy_i: int | None = None
    last_sell_i: int | None = None
    last_trade_i: int | None = None

    for i in range(len(matrix)):
        price = close[i]
        if price <= 0 or np.isnan(price):
            continue
        ts = idx[i]

        if last_trade_i is not None and i - last_trade_i < TRADE_MIN_GAP_BARS:
            continue

        if qty > 0:
            is_stop = _trigger_at(stop_mask, i) if rules.sell_stop else False
            is_sell = _trigger_at(s_mask, i)
            if is_stop or is_sell:
                if last_sell_i is not None:
                    if (ts - idx[last_sell_i]).total_seconds() < SELL_COOLDOWN_SEC:
                        continue
                sig = SIGNAL_SELL_STOP if is_stop else SIGNAL_SELL_UPPER
                events.append((ts, "sell", sig))
                qty = 0.0
                last_sell_i = i
                last_trade_i = i
                continue

        if _trigger_at(b_mask, i) and qty <= 0:
            if last_buy_i is not None:
                if (ts - idx[last_buy_i]).total_seconds() < BUY_COOLDOWN_SEC:
                    continue
            events.append((ts, "buy", SIGNAL_BUY_LOWER))
            qty = 1.0
            last_buy_i = i
            last_trade_i = i

    return events


def backtest_rules(
    matrix: pd.DataFrame,
    rules: DiscoveredRules,
    df_1d: pd.DataFrame,
    df_1h: pd.DataFrame,
    entry_ohlc: pd.DataFrame,
) -> tuple[float, int]:
    """
    HTML 시뮬과 동일한 run_backtest 로직으로 수익률 계산.
    """
    from simulation import run_backtest
    import strategy as st

    df = entry_ohlc.loc[matrix.index].copy()
    df["signal"] = ""
    df["point"] = 0
    df["action"] = ""
    df["trend"] = ""

    for ts, action, sig in generate_trade_events(matrix, rules):
        if ts not in df.index:
            continue
        trend_at = st.get_trend_at(df_1d, df_1h, ts)
        df.at[ts, "signal"] = sig
        df.at[ts, "point"] = 1
        df.at[ts, "action"] = action
        df.at[ts, "trend"] = trend_at

    res = run_backtest(df, df_1d, df_1h, config_name=rules.name)
    return res.total_return_pct, res.trade_count


def _evaluate_train(
    train: pd.DataFrame,
    rules: DiscoveredRules,
    df_1d: pd.DataFrame,
    df_1h: pd.DataFrame,
    entry_ohlc: pd.DataFrame,
) -> tuple[float, int, float]:
    """학습 구간 수익·거래수·목적함수 점수."""
    ret, tc = backtest_rules(train, rules, df_1d, df_1h, entry_ohlc)
    return ret, tc, discover_score(ret, tc)


def _baseline_rules() -> DiscoveredRules:
    """다봉 BB 하단 돌파 + 상단 돌파 기준선."""
    p3 = interval_prefix(ENTRY_INTERVAL)
    return DiscoveredRules(
        name="baseline_bb_mtf",
        buy_all=[
            f"{p3}:cross_up_lower",
            f"{interval_prefix(60)}:ichi_tk_bull",
            f"{interval_prefix(1440)}:!ichi_below_cloud",
        ],
        sell_all=[
            f"{p3}:cross_up_upper",
            f"{interval_prefix(60)}:cross_up_upper",
        ],
        sell_stop=[f"{p3}:cross_down_lower"],
    )


def _seed_from_combination_report() -> DiscoveredRules | None:
    """combination_report.json 제안 규칙."""
    path = Path(__file__).parent / "combination_report.json"
    if not path.exists():
        return None
    data = json.loads(path.read_text(encoding="utf-8"))
    sug = data.get("suggested_rules") or {}
    buy_all = list(sug.get("buy_all") or [])
    if not buy_all:
        return None
    return DiscoveredRules(
        name="combination_seed",
        buy_all=buy_all,
        buy_any=[list(g) for g in (sug.get("buy_any") or []) if g],
        sell_all=list(sug.get("sell_all") or [f"{interval_prefix(ENTRY_INTERVAL)}:cross_up_upper"]),
        sell_stop=list(sug.get("sell_stop") or []),
    )


def greedy_search(
    matrix: pd.DataFrame,
    train_end: int,
    pool: list[str],
    seed: DiscoveredRules,
    df_1d: pd.DataFrame,
    df_1h: pd.DataFrame,
    entry_ohlc: pd.DataFrame,
    max_buy: int = 8,
    max_sell: int = 6,
    max_stop: int = 2,
) -> DiscoveredRules:
    """학습 구간 수익률을 올리도록 매수/매도 조건을 탐욕적으로 확장."""
    train = matrix.iloc[:train_end]
    best = DiscoveredRules(
        name=seed.name,
        buy_all=list(seed.buy_all),
        buy_any=[list(g) for g in seed.buy_any],
        sell_all=list(seed.sell_all),
        sell_stop=list(seed.sell_stop),
    )
    best_ret, best_tc, best_score = _evaluate_train(
        train, best, df_1d, df_1h, entry_ohlc
    )

    buy_pool = [
        p
        for p in pool
        if not is_blocked_buy_predicate(p) and not is_chase_buy_predicate(p)
    ]
    sell_pool = [p for p in pool if not is_weak_sell_predicate(p)]

    improved = True
    while improved:
        improved = False
        # 매수 AND 추가/제거
        for pred in buy_pool:
            if pred in best.buy_all:
                trial_all = [p for p in best.buy_all if p != pred]
            else:
                if len(best.buy_all) >= max_buy:
                    continue
                trial_all = best.buy_all + [pred]
            trial = DiscoveredRules(
                name="trial",
                buy_all=trial_all,
                buy_any=best.buy_any,
                sell_all=best.sell_all,
                sell_stop=best.sell_stop,
            )
            ret, tc, score = _evaluate_train(
                train, trial, df_1d, df_1h, entry_ohlc
            )
            if score > best_score:
                best_ret, best_tc, best_score = ret, tc, score
                best.buy_all = trial_all
                improved = True

        # 매도 AND
        for pred in sell_pool:
            if pred in best.sell_all:
                trial_s = [p for p in best.sell_all if p != pred]
            else:
                if len(best.sell_all) >= max_sell:
                    continue
                trial_s = best.sell_all + [pred]
            trial = DiscoveredRules(
                name="trial",
                buy_all=best.buy_all,
                buy_any=best.buy_any,
                sell_all=trial_s,
                sell_stop=best.sell_stop,
            )
            ret, tc, score = _evaluate_train(
                train, trial, df_1d, df_1h, entry_ohlc
            )
            if score > best_score:
                best_ret, best_tc, best_score = ret, tc, score
                best.sell_all = trial_s
                improved = True

        # 손절
        stop_pool = [
            p
            for p in pool
            if "cross_down_lower" in p
            and not is_level_state_predicate(p)
        ]
        for pred in stop_pool:
            if pred in best.sell_stop:
                trial_st = [p for p in best.sell_stop if p != pred]
            else:
                if len(best.sell_stop) >= max_stop:
                    continue
                trial_st = best.sell_stop + [pred]
            trial = DiscoveredRules(
                name="trial",
                buy_all=best.buy_all,
                buy_any=best.buy_any,
                sell_all=best.sell_all,
                sell_stop=trial_st,
            )
            ret, tc, score = _evaluate_train(
                train, trial, df_1d, df_1h, entry_ohlc
            )
            if score > best_score:
                best_ret, best_tc, best_score = ret, tc, score
                best.sell_stop = trial_st
                improved = True

    return sanitize_rules(best)


def try_buy_any_branches(
    matrix: pd.DataFrame,
    train_end: int,
    base: DiscoveredRules,
    pool: list[str],
    df_1d: pd.DataFrame,
    df_1h: pd.DataFrame,
    entry_ohlc: pd.DataFrame,
    max_branches: int = 8,
) -> DiscoveredRules:
    """매수 OR 분기: 다른 봉의 cross_up_lower / hammer 등."""
    train = matrix.iloc[:train_end]
    triggers = [
        p
        for p in pool
        if p.endswith(":cross_up_lower")
        or p.endswith(":hammer")
        or p.endswith(":bb_zone_bottom")
        or p.endswith(":ichi_tk_cross_up")
    ]
    best = DiscoveredRules(
        name=base.name,
        buy_all=list(base.buy_all),
        buy_any=[list(g) for g in base.buy_any],
        sell_all=list(base.sell_all),
        sell_stop=list(base.sell_stop),
    )
    best_ret, best_tc, best_score = _evaluate_train(
        train, best, df_1d, df_1h, entry_ohlc
    )

    for pred in triggers[:max_branches]:
        if pred in best.buy_all:
            continue
        trial = DiscoveredRules(
            name="trial_or",
            buy_all=[],
            buy_any=[list(best.buy_all), [pred]],
            sell_all=best.sell_all,
            sell_stop=best.sell_stop,
        )
        if not trial.buy_any[0]:
            trial.buy_any = [[pred]]
        ret, tc, score = _evaluate_train(
            train, trial, df_1d, df_1h, entry_ohlc
        )
        if score > best_score:
            best_ret, best_score = ret, score
            best = sanitize_rules(trial)
            best.name = "discovered_or"

    return sanitize_rules(best)


def random_search_refine(
    matrix: pd.DataFrame,
    train_end: int,
    pool: list[str],
    seed: DiscoveredRules,
    df_1d: pd.DataFrame,
    df_1h: pd.DataFrame,
    entry_ohlc: pd.DataFrame,
    iterations: int = 1200,
) -> DiscoveredRules:
    """무작위 변형으로 국소 최적 보완."""
    train = matrix.iloc[:train_end]
    best = seed
    best_ret, best_tc, best_score = _evaluate_train(
        train, best, df_1d, df_1h, entry_ohlc
    )
    rng = random.Random(42)
    buy_pool = [
        p
        for p in pool
        if not is_blocked_buy_predicate(p) and not is_chase_buy_predicate(p)
    ]
    sell_pool = [p for p in pool if not is_weak_sell_predicate(p)]

    for _ in range(iterations):
        trial = DiscoveredRules(
            name="rand",
            buy_all=[p for p in best.buy_all],
            buy_any=[list(g) for g in best.buy_any],
            sell_all=[p for p in best.sell_all],
            sell_stop=[p for p in best.sell_stop],
        )
        action = rng.choice(["add_buy", "drop_buy", "add_sell", "drop_sell", "swap_buy"])
        if action == "add_buy" and len(trial.buy_all) < 6 and buy_pool:
            p = rng.choice(buy_pool)
            if p not in trial.buy_all:
                trial.buy_all.append(p)
        elif action == "drop_buy" and trial.buy_all:
            trial.buy_all.pop(rng.randrange(len(trial.buy_all)))
        elif action == "add_sell" and len(trial.sell_all) < 5 and sell_pool:
            p = rng.choice(sell_pool)
            if p not in trial.sell_all:
                trial.sell_all.append(p)
        elif action == "drop_sell" and trial.sell_all:
            trial.sell_all.pop(rng.randrange(len(trial.sell_all)))
        elif action == "swap_buy" and buy_pool:
            if trial.buy_all:
                trial.buy_all[rng.randrange(len(trial.buy_all))] = rng.choice(buy_pool)
        trial = sanitize_rules(trial)
        ret, tc, score = _evaluate_train(
            train, trial, df_1d, df_1h, entry_ohlc
        )
        if score > best_score:
            best_ret, best_score = ret, score
            best = trial
    best.name = "discovered_refined"
    return sanitize_rules(best)


def discover_rules(frames: dict[int, pd.DataFrame]) -> DiscoveredRules:
    """전체 탐색 파이프라인."""
    print("특징 행렬 생성 (모든 봉·캔들 위치/높이)...")
    from config import ENTRY_INTERVAL, TREND_INTERVAL_1D, TREND_INTERVAL_1H

    entry_raw = frames[ENTRY_INTERVAL]
    df_1d = frames.get(TREND_INTERVAL_1D)
    if df_1d is None or df_1d.empty:
        df_1d = entry_raw
    df_1h = frames.get(TREND_INTERVAL_1H)
    if df_1h is None or df_1h.empty:
        df_1h = entry_raw

    matrix = build_master_feature_matrix(frames)
    matrix = matrix.iloc[21:].copy()
    entry_ohlc = entry_raw.iloc[21:].loc[matrix.index]
    n = len(matrix)
    train_end = int(n * 0.7)
    intervals = sorted(frames.keys())
    pool = generate_predicate_pool(intervals)
    print(f"  샘플 {n}봉 | 학습 {train_end} | predicate 후보 {len(pool)}개")

    baseline = _discovery_seed()
    br, bt = backtest_rules(
        matrix.iloc[:train_end], baseline, df_1d, df_1h, entry_ohlc
    )
    print(f"  시드 규칙: {baseline.name} (하단돌파 매수·상단돌파 매도)")
    bf, _ = backtest_rules(matrix, baseline, df_1d, df_1h, entry_ohlc)
    print(f"  기준선: 학습 {br:+.2f}% | 전체 {bf:+.2f}%")

    print("1단계: 탐욕적 AND 확장...")
    g1 = greedy_search(matrix, train_end, pool, baseline, df_1d, df_1h, entry_ohlc)
    r1, _ = backtest_rules(matrix.iloc[:train_end], g1, df_1d, df_1h, entry_ohlc)
    print(f"       학습 {r1:+.2f}% | buy={g1.buy_all} sell={g1.sell_all}")

    print("2단계: 매수 OR 분기(다른 봉 트리거)...")
    g2 = try_buy_any_branches(matrix, train_end, g1, pool, df_1d, df_1h, entry_ohlc)
    r2, _ = backtest_rules(matrix.iloc[:train_end], g2, df_1d, df_1h, entry_ohlc)
    print(f"       학습 {r2:+.2f}%")

    print("3단계: 무작위 정밀 탐색...")
    best = g2 if r2 >= r1 else g1
    g3 = random_search_refine(matrix, train_end, pool, best, df_1d, df_1h, entry_ohlc, iterations=1200)
    g3 = sanitize_rules(g3)
    train_ret, t_cnt = backtest_rules(
        matrix.iloc[:train_end], g3, df_1d, df_1h, entry_ohlc
    )
    test_ret, _ = backtest_rules(
        matrix.iloc[train_end:], g3, df_1d, df_1h, entry_ohlc
    )
    full_ret, full_cnt = backtest_rules(matrix, g3, df_1d, df_1h, entry_ohlc)

    g3.train_return_pct = train_ret
    g3.test_return_pct = test_ret
    g3.full_return_pct = full_ret
    g3.trade_count = full_cnt
    g3.name = "discovered_best"

    print(f"\n최종 규칙 ({g3.name})")
    print(f"  매수 AND: {g3.buy_all}")
    if g3.buy_any:
        print(f"  매수 OR:  {g3.buy_any}")
    print(f"  매도 AND: {g3.sell_all}")
    if g3.sell_stop:
        print(f"  손절:     {g3.sell_stop}")
    print(
        f"  학습 {train_ret:+.2f}% | 검증 {test_ret:+.2f}% | 전체 {full_ret:+.2f}% ({full_cnt}건)"
    )
    return g3


def save_rules(rules: DiscoveredRules, path: Path = RULES_FILE) -> None:
    path.write_text(json.dumps(asdict(rules), ensure_ascii=False, indent=2), encoding="utf-8")


def rules_have_buy(rules: DiscoveredRules) -> bool:
    """매수 규칙이 하나라도 있는지."""
    if rules.buy_all:
        return True
    return any(bool(g) for g in rules.buy_any)


def load_rules(path: Path = RULES_FILE) -> DiscoveredRules | None:
    if not path.exists():
        return None
    data = json.loads(path.read_text(encoding="utf-8"))
    rules = DiscoveredRules(**{k: data[k] for k in asdict(DiscoveredRules()).keys() if k in data})
    if not rules_have_buy(rules):
        return None
    return rules


def load_frames(monitor) -> dict[int, pd.DataFrame]:
    from mtf_bb import load_frames_from_db

    return load_frames_from_db(monitor, SYMBOL)