"""Ground Truth 매수·매도 타점 생성 (1단계 · 0단계 sim 입력)."""

from __future__ import annotations

import json
from dataclasses import asdict, dataclass
from datetime import datetime
from pathlib import Path
from typing import Any

import pandas as pd

from bithumb.data.candle_loader import load_candles
from bithumb.data.intervals import interval_label
from bithumb.ground_truth.pnl import simulate_gt_pnl
from bithumb.ground_truth.breakout import find_breakout_buy_pivots
from bithumb.ground_truth.divergence import find_divergence_signals
from bithumb.ground_truth.pullback import find_pullback_buy_pivots
from bithumb.ground_truth.zigzag import Pivot, find_zigzag_pivots


@dataclass(frozen=True)
class GtParams:
    """Ground Truth 생성 파라미터."""

    interval_min: int
    lookback_days: int
    zigzag_reversal_pct: float
    min_leg_pct: float
    pullback_min_pct: float = 1.5
    pullback_local_order: int = 10
    breakout_buffer_pct: float = 0.1
    breakout_consolidation_bars: int = 200
    breakout_min_rally_pct: float = 2.0
    div_local_order: int = 20
    div_min_bars_between: int = 1500
    div_min_rsi_diff: float = 5.0
    div_min_future_move_pct: float = 4.0
    chart_tier: str = "v3"


def _tier_flags(tier: str) -> tuple[bool, bool, bool]:
    """차트 버전별 보조 신호 포함 여부 (눌림목, 돌파, 다이버전스).

    v1: ZigZag 스윙만 (레그당 1매수·1매도 최소)
    v2: 스윙 + 눌림목
    v3: v2 + 돌파 + 다이버전스
    """
    tier = tier.lower()
    if tier == "v1":
        return False, False, False
    if tier == "v2":
        return True, False, False
    return True, True, True


@dataclass
class GtLeg:
    """매수→매도 1레그 (최대 스윙 수익 구간)."""

    leg_id: int
    buy_datetime: str
    buy_price: float
    buy_bar_index: int
    sell_datetime: str
    sell_price: float
    sell_bar_index: int
    leg_pct: float
    bars_held: int


def build_ground_truth(
    db_path: Path,
    symbol: str,
    coin_name: str,
    params: GtParams,
    initial_cash_krw: float = 400_000.0,
    fee_rate: float = 0.0005,
) -> dict[str, Any]:
    """최근 1년 구간에서 사후 최적 스윙 레그(1매수·1매도) GT를 생성한다.

    미래 데이터를 사용해 ZigZag 스윙 저점 매수·고점 매도 쌍을 찾는다.
    1단계 벤치마크: 최대 스윙 수익을 포착하는 타점.

    Args:
        db_path: SQLite 경로.
        symbol: 코인 심볼.
        coin_name: 코인 이름.
        params: GT 파라미터.
        initial_cash_krw: 수익률 계산 초기 자본 (1년 시작 시점).
        fee_rate: 거래 수수료율.

    Returns:
        JSON 직렬화 가능한 GT 결과 dict.
    """
    df = load_candles(
        db_path=db_path,
        symbol=symbol,
        interval_min=params.interval_min,
        lookback_days=params.lookback_days,
    )

    pivots = find_zigzag_pivots(df, reversal_pct=params.zigzag_reversal_pct)
    legs = _pivots_to_legs(pivots, min_leg_pct=params.min_leg_pct)
    leg_dicts = [asdict(leg) for leg in legs]
    include_pullback, include_breakout, include_divergence = _tier_flags(params.chart_tier)

    pullback_buys: list[Pivot] = []
    if include_pullback:
        pullback_buys = find_pullback_buy_pivots(
            df,
            legs=legs,
            min_pullback_pct=params.pullback_min_pct,
            local_order=params.pullback_local_order,
        )

    breakout_buys = []
    if include_breakout:
        breakout_buys = find_breakout_buy_pivots(
            df,
            legs=legs,
            pullback_buys=pullback_buys,
            breakout_buffer_pct=params.breakout_buffer_pct,
            consolidation_bars=params.breakout_consolidation_bars,
            min_rally_to_sell_pct=params.breakout_min_rally_pct,
        )

    div_buys: list = []
    div_sells: list = []
    if include_divergence:
        div_buys, div_sells = find_divergence_signals(
            df,
            local_order=params.div_local_order,
            min_bars_between=params.div_min_bars_between,
            min_rsi_diff=params.div_min_rsi_diff,
            min_future_move_pct=params.div_min_future_move_pct,
        )

    mode_map = {
        "v1": "optimal_swing_legs",
        "v2": "optimal_swing_legs_with_pullback",
        "v3": "optimal_swing_legs_with_pullback_breakout_divergence",
    }
    mode = mode_map.get(params.chart_tier.lower(), mode_map["v3"])

    signals = _build_signals(legs, pullback_buys, breakout_buys, div_buys, div_sells)
    summary = _summarize(legs, signals)
    pnl = simulate_gt_pnl(leg_dicts, initial_cash_krw=initial_cash_krw, fee_rate=fee_rate)

    return {
        "meta": {
            "symbol": symbol.upper(),
            "coin_name": coin_name,
            "interval_min": params.interval_min,
            "interval_label": interval_label(params.interval_min),
            "lookback_days": params.lookback_days,
            "chart_tier": params.chart_tier.lower(),
            "mode": mode,
            "zigzag_reversal_pct": params.zigzag_reversal_pct,
            "min_leg_pct": params.min_leg_pct,
            "pullback_min_pct": params.pullback_min_pct,
            "initial_cash_krw": initial_cash_krw,
            "generated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            "data_from": str(df["datetime"].min()),
            "data_to": str(df["datetime"].max()),
            "bar_count": len(df),
            "pivot_count": len(pivots),
            "pullback_buy_count": len(pullback_buys),
            "breakout_buy_count": len(breakout_buys),
            "breakout_buffer_pct": params.breakout_buffer_pct,
            "divergence_buy_count": len(div_buys),
            "divergence_sell_count": len(div_sells),
        },
        "legs": leg_dicts,
        "signals": signals,
        "summary": summary,
        "pnl": pnl,
    }


def save_ground_truth(result: dict[str, Any], output_path: Path) -> Path:
    """GT 결과를 JSON으로 저장한다."""
    output_path.parent.mkdir(parents=True, exist_ok=True)
    with output_path.open("w", encoding="utf-8") as fp:
        json.dump(result, fp, ensure_ascii=False, indent=2)
    return output_path


def _pivots_to_legs(pivots: list[Pivot], min_leg_pct: float) -> list[GtLeg]:
    """스윙 저점→고점을 1매수·1매도 레그로 변환한다."""
    legs: list[GtLeg] = []
    leg_id = 0
    i = 0

    while i < len(pivots) - 1:
        buy_pivot = pivots[i]
        sell_pivot = pivots[i + 1]

        if buy_pivot.side != "low" or sell_pivot.side != "high":
            i += 1
            continue
        if sell_pivot.bar_index <= buy_pivot.bar_index:
            i += 1
            continue

        leg_pct = (sell_pivot.price - buy_pivot.price) / buy_pivot.price * 100.0
        if leg_pct < min_leg_pct:
            i += 1
            continue

        leg_id += 1
        legs.append(
            GtLeg(
                leg_id=leg_id,
                buy_datetime=buy_pivot.datetime.strftime("%Y-%m-%d %H:%M:%S"),
                buy_price=round(buy_pivot.price, 2),
                buy_bar_index=buy_pivot.bar_index,
                sell_datetime=sell_pivot.datetime.strftime("%Y-%m-%d %H:%M:%S"),
                sell_price=round(sell_pivot.price, 2),
                sell_bar_index=sell_pivot.bar_index,
                leg_pct=round(leg_pct, 2),
                bars_held=sell_pivot.bar_index - buy_pivot.bar_index,
            )
        )
        i += 2

    return legs


def _build_signals(
    legs: list[GtLeg],
    pullback_buys: list[Pivot],
    breakout_buys: list,
    div_buys: list,
    div_sells: list,
) -> list[dict[str, Any]]:
    """스윙·눌림목·돌파·다이버전스 신호를 통합한다."""
    signals: list[dict[str, Any]] = []
    buy_marker_id = 0
    sell_marker_id = 0

    existing_buy_bars: set[int] = {leg.buy_bar_index for leg in legs}
    existing_sell_bars: set[int] = {leg.sell_bar_index for leg in legs}
    nearby_tolerance = 120

    for leg in legs:
        buy_marker_id += 1
        signals.append(
            {
                "marker_id": buy_marker_id,
                "leg_id": leg.leg_id,
                "side": "buy",
                "signal_type": "swing_low",
                "datetime": leg.buy_datetime,
                "price": leg.buy_price,
                "bar_index": leg.buy_bar_index,
            }
        )
        sell_marker_id += 1
        existing_sell_bars.add(leg.sell_bar_index)
        signals.append(
            {
                "marker_id": sell_marker_id,
                "leg_id": leg.leg_id,
                "side": "sell",
                "signal_type": "swing_high",
                "datetime": leg.sell_datetime,
                "price": leg.sell_price,
                "bar_index": leg.sell_bar_index,
                "leg_pct": leg.leg_pct,
            }
        )

    for pivot in pullback_buys:
        if _is_near_existing_buy(pivot.bar_index, existing_buy_bars, nearby_tolerance):
            continue
        buy_marker_id += 1
        existing_buy_bars.add(pivot.bar_index)
        signals.append(
            {
                "marker_id": buy_marker_id,
                "leg_id": None,
                "side": "buy",
                "signal_type": "pullback",
                "datetime": pivot.datetime.strftime("%Y-%m-%d %H:%M:%S"),
                "price": round(pivot.price, 2),
                "bar_index": pivot.bar_index,
            }
        )

    for breakout in breakout_buys:
        if _is_near_existing_buy(breakout.bar_index, existing_buy_bars, nearby_tolerance):
            continue
        buy_marker_id += 1
        existing_buy_bars.add(breakout.bar_index)
        signals.append(
            {
                "marker_id": buy_marker_id,
                "leg_id": breakout.leg_id,
                "side": "buy",
                "signal_type": "breakout",
                "datetime": breakout.datetime.strftime("%Y-%m-%d %H:%M:%S"),
                "price": breakout.price,
                "bar_index": breakout.bar_index,
                "resistance_price": breakout.resistance_price,
            }
        )

    div_tolerance = 400
    for div in div_buys:
        if _is_near_bar(div.bar_index, existing_buy_bars, div_tolerance):
            continue
        buy_marker_id += 1
        existing_buy_bars.add(div.bar_index)
        signals.append(_divergence_to_dict(div, buy_marker_id, "div_bull"))

    for div in div_sells:
        if _is_near_bar(div.bar_index, existing_sell_bars, div_tolerance):
            continue
        sell_marker_id += 1
        existing_sell_bars.add(div.bar_index)
        signals.append(_divergence_to_dict(div, sell_marker_id, "div_bear"))

    signals.sort(key=lambda s: (s["bar_index"], _signal_sort_key(s)))
    return signals


def _divergence_to_dict(div, marker_id: int, signal_type: str) -> dict[str, Any]:
    """DivergenceSignal을 GT signal dict로 변환한다."""
    return {
        "marker_id": marker_id,
        "leg_id": None,
        "side": div.side,
        "signal_type": signal_type,
        "datetime": div.datetime.strftime("%Y-%m-%d %H:%M:%S"),
        "price": div.price,
        "bar_index": div.bar_index,
        "indicator": div.indicator,
        "price_prev": div.price_prev,
        "ind_prev": div.ind_prev,
        "ind_curr": div.ind_curr,
    }


def _signal_sort_key(signal: dict[str, Any]) -> int:
    """동일 봉에서 신호 유형 정렬 우선순위."""
    order = {
        "swing_low": 0,
        "pullback": 1,
        "breakout": 2,
        "div_bull": 3,
        "swing_high": 4,
        "div_bear": 5,
    }
    return order.get(signal.get("signal_type", ""), 9)


def _is_near_bar(bar_index: int, existing_bars: set[int], tolerance: int) -> bool:
    """기존 타점과 너무 가까우면 보조 신호를 제외한다."""
    for existing in existing_bars:
        if abs(bar_index - existing) <= tolerance:
            return True
    return False


def _is_near_existing_buy(bar_index: int, existing_bars: set[int], tolerance: int) -> bool:
    """기존 매수와 너무 가까우면 보조 매수를 제외한다."""
    return _is_near_bar(bar_index, existing_bars, tolerance)


def _summarize(legs: list[GtLeg], signals: list[dict[str, Any]]) -> dict[str, Any]:
    """GT 요약 통계."""
    buy_count = sum(1 for s in signals if s["side"] == "buy")
    sell_count = sum(1 for s in signals if s["side"] == "sell")
    pullback_count = sum(1 for s in signals if s.get("signal_type") == "pullback")
    breakout_count = sum(1 for s in signals if s.get("signal_type") == "breakout")
    div_buy_count = sum(1 for s in signals if s.get("signal_type") == "div_bull")
    div_sell_count = sum(1 for s in signals if s.get("signal_type") == "div_bear")

    if not legs:
        return {
            "leg_count": 0,
            "buy_count": buy_count,
            "sell_count": sell_count,
            "pullback_buy_count": pullback_count,
            "breakout_buy_count": breakout_count,
            "divergence_buy_count": div_buy_count,
            "divergence_sell_count": div_sell_count,
            "avg_leg_pct": 0.0,
            "median_leg_pct": 0.0,
            "max_leg_pct": 0.0,
            "min_leg_pct": 0.0,
            "avg_bars_held": 0.0,
        }

    pcts = [leg.leg_pct for leg in legs]
    bars = [leg.bars_held for leg in legs]
    return {
        "leg_count": len(legs),
        "buy_count": buy_count,
        "sell_count": sell_count,
        "pullback_buy_count": pullback_count,
        "breakout_buy_count": breakout_count,
        "divergence_buy_count": div_buy_count,
        "divergence_sell_count": div_sell_count,
        "avg_leg_pct": round(sum(pcts) / len(pcts), 2),
        "median_leg_pct": round(float(pd.Series(pcts).median()), 2),
        "max_leg_pct": round(max(pcts), 2),
        "min_leg_pct": round(min(pcts), 2),
        "avg_bars_held": round(sum(bars) / len(bars), 1),
    }