"""
총자산 대비 최적 매수율(비중) · 현금 한도 · leg 상위·EV/WF 통과 대형 매수.
"""

from __future__ import annotations

import json
from datetime import datetime
from pathlib import Path
from typing import Any

from config import (
    GT_BUY_PCT_LARGE_LEG,
    GT_BUY_PCT_SMALL_LEG,
    GT_INITIAL_CASH_KRW,
    GT_LARGE_LEG_TOP_PCT,
    GT_MIN_ORDER_KRW,
    LIVE_BUY_PCT_LARGE,
    LIVE_BUY_PCT_SMALL,
    MATCH_GT_TOLERANCE_MIN,
    SIM_FEE_STRESS_MULT,
    SIM_GO_MIN_HOLDOUT_EV,
    SIM_GO_MIN_HOLDOUT_PF,
    SIM_GO_WF_POSITIVE_RATIO,
    SIM_WALK_FORWARD_MIN_MONTHS,
    TRADING_FEE_RATE,
)
from deepcoin.matching.load_rules import load_matched_rules
from deepcoin.paths import MATCHING_FIRE_OUTCOMES, MATCHING_MATCHED_RULES


def portfolio_totals(
    cash: float,
    qty: float,
    price: float,
) -> tuple[float, float, float]:
    """
    총보유자산·코인평가·가용현금(=총자산-평가액)을 계산합니다.

    Args:
        cash: 현금.
        qty: 보유 수량.
        price: 평가·체결가.

    Returns:
        (total_asset_krw, holding_value_krw, cash_krw).
    """
    holding = qty * price
    total = cash + holding
    return total, holding, cash


def optimal_weight_share(weight: float, weight_sum_remaining: float) -> float:
    """
    leg 내 남은 매수 비중 대비 이번 체결 최적 매수율(0~1).

    Args:
        weight: 이번 타점 weight.
        weight_sum_remaining: 동일 leg 남은 매수 weight 합.

    Returns:
        비중 비율.
    """
    if weight_sum_remaining > 0:
        return weight / weight_sum_remaining
    return 1.0


def compute_buy_amount_krw(
    cash: float,
    qty: float,
    price: float,
    weight: float,
    weight_sum_remaining: float,
    *,
    asset_pct_scale: float,
    min_order_krw: float = GT_MIN_ORDER_KRW,
    fee_rate: float = TRADING_FEE_RATE,
) -> float:
    """
    총자산 × (최적 매수율 × scale)을 목표로, 가용 현금을 넘지 않게 매수 원화를 산출합니다.

    Args:
        cash: 현금.
        qty: 보유 수량.
        price: 체결가.
        weight: 타점 비중.
        weight_sum_remaining: leg 내 남은 매수 weight 합.
        asset_pct_scale: leg·규칙 티어(대형/소형) 스케일.
        min_order_krw: 최소 주문 원화.
        fee_rate: 수수료율.

    Returns:
        매수 원화(0이면 미체결).
    """
    if price <= 0:
        return 0.0
    total_asset, _, _ = portfolio_totals(cash, qty, price)
    budget = max(cash / (1.0 + fee_rate), 0.0)
    opt_rate = optimal_weight_share(weight, weight_sum_remaining) * asset_pct_scale
    target = total_asset * opt_rate
    amount = min(target, budget)
    if budget >= min_order_krw and 0 < amount < min_order_krw:
        amount = min(min_order_krw, budget)
    return round(max(amount, 0.0), 0)


def top_leg_ids_by_forward_return(
    trades: list[dict[str, Any]],
    top_pct: float = GT_LARGE_LEG_TOP_PCT,
) -> set[int]:
    """
    leg별 최대 forward_return 기준 상위 n% leg_id 집합.

    Args:
        trades: GT trade dict.
        top_pct: 상위 비율(0~1).

    Returns:
        대형 매수 leg_id set.
    """
    leg_ret: dict[int, float] = {}
    for t in trades:
        if t.get("action") != "sell":
            continue
        lid = int(t.get("leg_id", 0))
        ret = float(t.get("forward_return_pct") or 0.0)
        leg_ret[lid] = max(leg_ret.get(lid, 0.0), ret)
    if not leg_ret:
        return set()
    ranked = sorted(leg_ret.items(), key=lambda x: x[1], reverse=True)
    n = max(1, int(len(ranked) * top_pct + 0.999999))
    return {lid for lid, _ in ranked[:n]}


def leg_asset_pct_scale(leg_id: int, large_legs: set[int]) -> float:
    """
    leg 티어에 따른 총자산 대비 매수 스케일.

    Args:
        leg_id: leg 번호.
        large_legs: 상위 leg 집합.

    Returns:
        GT_BUY_PCT_LARGE_LEG 또는 GT_BUY_PCT_SMALL_LEG.
    """
    if leg_id in large_legs:
        return float(GT_BUY_PCT_LARGE_LEG)
    return float(GT_BUY_PCT_SMALL_LEG)


def _parse_dt(dt: str) -> datetime:
    return datetime.fromisoformat(str(dt).replace("Z", "+00:00")[:19])


def nearest_gt_leg_id(
    dt: str,
    gt_trades: list[dict[str, Any]],
    tolerance_min: int = MATCH_GT_TOLERANCE_MIN,
) -> int | None:
    """
    시각에 가장 가까운 GT trade의 leg_id (매수 우선).

    Args:
        dt: 발화 시각.
        gt_trades: GT trades.
        tolerance_min: 허용 분.

    Returns:
        leg_id 또는 None.
    """
    if not gt_trades:
        return None
    t0 = _parse_dt(dt)
    best_buy: int | None = None
    best_buy_min = float(tolerance_min) + 1.0
    best_any: int | None = None
    best_any_min = float(tolerance_min) + 1.0
    for t in gt_trades:
        try:
            t1 = _parse_dt(t["dt"])
        except ValueError:
            continue
        delta = abs((t0 - t1).total_seconds()) / 60.0
        if delta > tolerance_min:
            continue
        lid = int(t.get("leg_id", 0))
        if t.get("action") == "buy" and delta < best_buy_min:
            best_buy_min = delta
            best_buy = lid
        if delta < best_any_min:
            best_any_min = delta
            best_any = lid
    return best_buy if best_buy is not None else best_any


_APPROVED_RULES_CACHE: set[str] | None = None


def load_ev_wf_approved_rule_ids(
    matched_path: Path | None = None,
    outcomes_path: Path | None = None,
) -> set[str]:
    """
    holdout EV·PF, walk-forward, 수수료 스트레스를 모두 통과한 rule_id.

    Args:
        matched_path: matched_rules.json.
        outcomes_path: fire_outcomes.csv.

    Returns:
        통과 rule_id set. 산출 불가 시 monitor_rules 전체 fallback.
    """
    global _APPROVED_RULES_CACHE
    if _APPROVED_RULES_CACHE is not None:
        return set(_APPROVED_RULES_CACHE)

    from deepcoin.matching.select_rules import _rule_metrics, _split_train_valid_holdout
    from deepcoin.matching.simulation import (
        evaluate_go_no_go,
        simulate_live_order_cap,
        walk_forward_by_month,
        walk_forward_summary,
    )

    mp = matched_path or MATCHING_MATCHED_RULES
    op = outcomes_path or MATCHING_FIRE_OUTCOMES
    matched = load_matched_rules(mp)
    rules = matched.get("monitor_rules") or []
    if not rules or not op.is_file():
        return {r["rule_id"] for r in rules}

    import pandas as pd

    from config import MATCH_FEE_RATE

    outcomes = pd.read_csv(op)
    outcomes["split"] = _split_train_valid_holdout(outcomes)
    wf_sum = walk_forward_summary(walk_forward_by_month(outcomes))
    fee_stress: dict[str, Any] = {}
    for rid in outcomes["rule_id"].unique():
        sub = outcomes[outcomes["rule_id"] == rid]
        from deepcoin.matching.simulation import _fee_adjust_ret

        adj = _fee_adjust_ret(sub["forward_ret_pct"], SIM_FEE_STRESS_MULT)
        fee_stress[rid] = _rule_metrics(sub.assign(forward_ret_pct=adj))
    monitor_ids = {r["rule_id"] for r in rules}
    live_cap = simulate_live_order_cap(
        outcomes, rule_ids=monitor_ids, holdout_only=True
    )
    go = evaluate_go_no_go(matched, wf_sum, fee_stress, live_cap)
    passed = {c["rule_id"] for c in go.get("checks", []) if c.get("pass")}
    if passed:
        _APPROVED_RULES_CACHE = passed
        return passed
    fallback = monitor_ids
    _APPROVED_RULES_CACHE = fallback
    return fallback


def live_buy_asset_pct_scale(
    rule_id: str,
    dt: str,
    gt_trades: list[dict[str, Any]],
    *,
    approved_rules: set[str],
    large_legs: set[int],
) -> float:
    """
    실거래·시뮬 매수: EV/WF 통과 규칙 + leg 상위만 대형 비율.

    Args:
        rule_id: 규칙 ID.
        dt: 체결 시각.
        gt_trades: GT trades.
        approved_rules: 통과 rule_id.
        large_legs: 상위 leg.

    Returns:
        LIVE_BUY_PCT_LARGE 또는 LIVE_BUY_PCT_SMALL(또는 0에 가까운 소형).
    """
    if rule_id not in approved_rules:
        return float(LIVE_BUY_PCT_SMALL)
    lid = nearest_gt_leg_id(dt, gt_trades)
    if lid is not None and lid in large_legs:
        return float(LIVE_BUY_PCT_LARGE)
    return float(LIVE_BUY_PCT_SMALL)


def attach_dynamic_buy_amounts(
    trades: list[dict[str, Any]],
    *,
    gt_trades: list[dict[str, Any]],
    approved_rules: set[str] | None = None,
    large_legs: set[int] | None = None,
    initial_cash: float = GT_INITIAL_CASH_KRW,
    default_weight: float = 1.0,
    fee_rate: float = TRADING_FEE_RATE,
) -> list[dict[str, Any]]:
    """
    시뮬 발화 trade dict에 amount_krw(총자산 비율·현금 한도)를 채웁니다.

    Args:
        trades: 시간순 {dt, action, price, rule_id, …}.
        gt_trades: GT leg 매칭용.
        approved_rules: EV/WF 통과 rule. None이면 전 규칙 대형 허용 안 함.
        large_legs: 상위 leg. None이면 GT에서 산출.
        initial_cash: 초기 현금.
        default_weight: 매수 weight 기본값.
        fee_rate: 수수료율.

    Returns:
        amount_krw가 채워진 동일 리스트.
    """
    if large_legs is None:
        large_legs = top_leg_ids_by_forward_return(gt_trades)
    if approved_rules is None:
        approved_rules = set()

    cash = float(initial_cash)
    qty = 0.0
    for t in sorted(trades, key=lambda x: x["dt"]):
        action = t.get("action", t.get("side", ""))
        price = float(t["price"])
        if price <= 0:
            continue
        if action == "buy":
            rid = str(t.get("rule_id", ""))
            scale = live_buy_asset_pct_scale(
                rid, t["dt"], gt_trades,
                approved_rules=approved_rules,
                large_legs=large_legs,
            )
            amount = compute_buy_amount_krw(
                cash,
                qty,
                price,
                float(t.get("weight", default_weight)),
                float(t.get("weight", default_weight)),
                asset_pct_scale=scale,
                fee_rate=fee_rate,
            )
            t["amount_krw"] = amount
            if amount > 0:
                fee = amount * fee_rate
                cash -= amount + fee
                qty += amount / price
        elif action == "sell" and qty > 1e-12:
            gross = qty * price
            t["amount_krw"] = round(gross, 0)
            fee = gross * fee_rate
            cash += gross - fee
            qty = 0.0
    return trades


def load_sizing_context_from_gt(
    gt_path: Path | None = None,
) -> tuple[list[dict[str, Any]], set[int], set[str]]:
    """
    GT JSON에서 trades, 상위 leg, EV/WF 통과 rule을 로드합니다.

    Args:
        gt_path: ground_truth_trades.json.

    Returns:
        (gt_trades, large_legs, approved_rules).
    """
    from deepcoin.paths import resolve_ground_truth_file

    p = gt_path or resolve_ground_truth_file()
    trades: list[dict[str, Any]] = []
    if p.is_file():
        data = json.loads(p.read_text(encoding="utf-8"))
        trades = data.get("trades") or []
    large = top_leg_ids_by_forward_return(trades)
    approved = load_ev_wf_approved_rule_ids()
    return trades, large, approved