"""
布林带均线策略 - 全参数组合扫描 (1-1000, 1-1000)

策略:
  - 阳线 + 先涨碰到均线(1m判断) → 开多
  - 持多: 碰上轨止盈
  - 阴线 + 先跌碰到均线(1m判断) → 平多开空
  - 持空: 碰下轨止盈

配置: 200U | 1%权益/单 | 万五手续费 | 90%返佣次日8点 | 100x杠杆 | 全仓

参数遍历: (0.5,0.5)(0.5,1)...(0.5,std_max), (1,0.5)(1,1)...(1,std_max), ...
         直至 (period_max, std_max)
"""
from __future__ import annotations

import os
import sys
import tempfile
import time
from collections import defaultdict
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path

sys.path.insert(0, str(Path(__file__).resolve().parents[0]))
sys.stdout.reconfigure(line_buffering=True)

import numpy as np
import pandas as pd

from strategy.bb_midline_backtest import BBMidlineConfig, run_bb_midline_backtest
from strategy.data_loader import get_1m_touch_direction, load_klines
from strategy.indicators import bollinger


def build_full_param_grid(
    period_min: float = 1.0,
    period_max: float = 1000.0,
    period_step: float = 1.0,
    std_min: float = 1.0,
    std_max: float = 1000.0,
    std_step: float = 1.0,
) -> list[tuple[int, float]]:
    """生成全量 (period, std) 组合，period 取整"""
    out = []
    p = period_min
    while p <= period_max:
        s = std_min
        while s <= std_max:
            out.append((max(1, int(round(p))), round(s, 2)))
            s += std_step
        p += period_step
    return sorted(set(out))


def stable_score(ret_pct: float, sharpe: float, dd_pct: float, n_trades: int) -> float:
    """收益稳定性评分"""
    sparse_penalty = -5.0 if n_trades < 200 else 0.0
    return ret_pct + sharpe * 12.0 - abs(dd_pct) * 0.8 + sparse_penalty


G_DF: pd.DataFrame | None = None
G_DF_1M: pd.DataFrame | None = None
G_USE_1M: bool = True
G_STEP_MIN: int = 5


def _init_worker(df_path: str, df_1m_path: str | None, use_1m: bool, step_min: int):
    global G_DF, G_DF_1M, G_USE_1M, G_STEP_MIN
    G_DF = pd.read_pickle(df_path)
    G_DF_1M = pd.read_pickle(df_1m_path) if (use_1m and df_1m_path) else None
    G_USE_1M = bool(use_1m)
    G_STEP_MIN = int(step_min)


def _eval_period_task(args: tuple[int, list[float]]) -> list[dict]:
    period, std_list = args
    assert G_DF is not None

    arr_touch_dir = None
    if G_USE_1M and G_DF_1M is not None:
        close = G_DF["close"].astype(float)
        bb_mid, _, _, _ = bollinger(close, period, 1.0)
        arr_touch_dir = get_1m_touch_direction(
            G_DF, G_DF_1M, bb_mid.values, kline_step_min=G_STEP_MIN
        )

    rows: list[dict] = []
    for std in std_list:
        cfg = BBMidlineConfig(
            bb_period=period,
            bb_std=float(std),
            initial_capital=200.0,
            margin_pct=0.01,
            leverage=100.0,
            cross_margin=True,
            fee_rate=0.0005,
            rebate_pct=0.90,
            rebate_hour_utc=0,
            fill_at_close=True,
            use_1m_touch_filter=G_USE_1M,
            kline_step_min=G_STEP_MIN,
        )
        result = run_bb_midline_backtest(
            G_DF,
            cfg,
            df_1m=G_DF_1M if G_USE_1M else None,
            arr_touch_dir_override=arr_touch_dir,
        )

        eq = result.equity_curve["equity"].dropna()
        if len(eq) == 0:
            final_eq = 0.0
            ret_pct = -100.0
            dd_u = -200.0
            dd_pct = 100.0
        else:
            final_eq = float(eq.iloc[-1])
            ret_pct = (final_eq - cfg.initial_capital) / cfg.initial_capital * 100.0
            dd_u = float((eq.astype(float) - eq.astype(float).cummax()).min())
            dd_pct = abs(dd_u) / cfg.initial_capital * 100.0

        n_trades = len(result.trades)
        win_rate = (
            sum(1 for t in result.trades if t.net_pnl > 0) / n_trades * 100.0
            if n_trades > 0
            else 0.0
        )
        pnl = result.daily_stats["pnl"].astype(float)
        sharpe = (
            float(pnl.mean() / pnl.std()) * np.sqrt(365.0) if pnl.std() > 0 else 0.0
        )
        score = stable_score(ret_pct, sharpe, dd_pct, n_trades)

        rows.append(
            {
                "period": period,
                "std": round(float(std), 2),
                "final_eq": final_eq,
                "ret_pct": ret_pct,
                "n_trades": n_trades,
                "win_rate": win_rate,
                "sharpe": sharpe,
                "max_dd_u": dd_u,
                "max_dd_pct": dd_pct,
                "stable_score": score,
            }
        )
    return rows


def evaluate_grid(
    params: list[tuple[int, float]],
    *,
    workers: int,
    df_path: str,
    df_1m_path: str | None,
    use_1m: bool,
    step_min: int,
) -> pd.DataFrame:
    by_period: dict[int, set[float]] = defaultdict(set)
    for p, s in params:
        by_period[int(p)].add(round(float(s), 2))

    tasks = [(p, sorted(stds)) for p, stds in sorted(by_period.items())]
    total_periods = len(tasks)
    total_combos = sum(len(stds) for _, stds in tasks)

    print(f"  评估 {total_combos:,} 组参数, {total_periods} 个 period, workers={workers}")
    start = time.time()
    rows: list[dict] = []
    done_periods = 0
    done_combos = 0

    with ProcessPoolExecutor(
        max_workers=workers,
        initializer=_init_worker,
        initargs=(df_path, df_1m_path, use_1m, step_min),
    ) as ex:
        future_map = {ex.submit(_eval_period_task, task): task for task in tasks}
        for fut in as_completed(future_map):
            period, stds = future_map[fut]
            res = fut.result()
            rows.extend(res)
            done_periods += 1
            done_combos += len(stds)
            if done_periods % max(1, total_periods // 20) == 0 or done_periods == total_periods:
                elapsed = time.time() - start
                print(f"    进度 {done_combos:,}/{total_combos:,} ({elapsed:.0f}s)")

    df = pd.DataFrame(rows)
    print(f"  完成, 用时 {time.time() - start:.1f}s")
    return df


def main():
    import argparse

    parser = argparse.ArgumentParser(description="布林带均线策略全参数扫描 (1-1000, 1-1000)")
    parser.add_argument(
        "--period-min", type=float, default=1.0, help="period 下限"
    )
    parser.add_argument(
        "--period-max", type=float, default=1000.0, help="period 上限"
    )
    parser.add_argument(
        "--period-step", type=float, default=10.0, help="period 步长 (建议10以缩短时间)"
    )
    parser.add_argument("--std-min", type=float, default=0.5, help="std 下限")
    parser.add_argument("--std-max", type=float, default=1000.0, help="std 上限")
    parser.add_argument(
        "--std-step", type=float, default=1.0, help="std 步长"
    )
    parser.add_argument(
        "-p", "--kline-period", default="5m", choices=["5m", "15m", "30m"]
    )
    parser.add_argument(
        "-j", "--workers", type=int, default=max(1, (os.cpu_count() or 4) - 1)
    )
    parser.add_argument("--no-1m", action="store_true", help="禁用 1m 触及方向过滤")
    parser.add_argument(
        "--source",
        default="bitmart",
        choices=["bitmart", "binance"],
        help="数据源",
    )
    parser.add_argument(
        "--quick",
        action="store_true",
        help="快速模式: period 1-200 step20, std 1-20 step2",
    )
    args = parser.parse_args()

    use_1m = not args.no_1m
    step_min = int(args.kline_period.replace("m", ""))

    if args.quick:
        args.period_min = 1.0
        args.period_max = 200.0
        args.period_step = 20.0
        args.std_min = 0.5
        args.std_max = 20.0
        args.std_step = 1.0
        print("  快速模式: period 1-200 step20, std 1-20 step2")

    out_dir = Path(__file__).resolve().parent / "strategy" / "results"
    out_dir.mkdir(parents=True, exist_ok=True)

    print("加载 K 线数据 (2020-01-01 ~ 2026-01-01)...")
    t0 = time.time()
    try:
        df = load_klines(args.kline_period, "2020-01-01", "2026-01-01", source=args.source)
        df_1m = (
            load_klines("1m", "2020-01-01", "2026-01-01", source=args.source)
            if use_1m
            else None
        )
    except Exception as e:
        alt = "binance" if args.source == "bitmart" else "bitmart"
        print(f"  {args.source} 加载失败 ({e}), 尝试 {alt}...")
        df = load_klines(args.kline_period, "2020-01-01", "2026-01-01", source=alt)
        df_1m = (
            load_klines("1m", "2020-01-01", "2026-01-01", source=alt)
            if use_1m
            else None
        )
        args.source = alt
    print(
        f"  {args.kline_period}: {len(df):,} 条"
        + (f", 1m: {len(df_1m):,} 条" if df_1m is not None else "")
        + f" | 数据源: {args.source} ({time.time()-t0:.1f}s)\n"
    )

    grid = build_full_param_grid(
        period_min=args.period_min,
        period_max=args.period_max,
        period_step=args.period_step,
        std_min=args.std_min,
        std_max=args.std_max,
        std_step=args.std_step,
    )
    print(f"参数网格: {len(grid):,} 组")
    print(
        f"  period: {args.period_min}~{args.period_max} step{args.period_step}, "
        f"std: {args.std_min}~{args.std_max} step{args.std_step}"
    )

    with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f_df:
        df.to_pickle(f_df.name)
        df_path = f_df.name
    df_1m_path = None
    if df_1m is not None:
        with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as f_1m:
            df_1m.to_pickle(f_1m.name)
            df_1m_path = f_1m.name

    try:
        result_df = evaluate_grid(
            grid,
            workers=args.workers,
            df_path=df_path,
            df_1m_path=df_1m_path,
            use_1m=use_1m,
            step_min=step_min,
        )
    finally:
        Path(df_path).unlink(missing_ok=True)
        if df_1m_path:
            Path(df_1m_path).unlink(missing_ok=True)

    if result_df.empty:
        print("无有效结果")
        return

    best_stable = result_df.sort_values("stable_score", ascending=False).iloc[0]
    best_return = result_df.sort_values("ret_pct", ascending=False).iloc[0]

    stamp = time.strftime("%Y%m%d_%H%M%S")
    csv_path = out_dir / f"bb_midline_full_grid_{args.kline_period}_{stamp}.csv"
    result_df.to_csv(csv_path, index=False)
    print(f"\n扫描结果已保存: {csv_path}")

    print("\n" + "=" * 90)
    print("布林带均线策略 | 2020-2025 | 200U | 1%权益/单 | 万五 | 90%返佣次日8点 | 100x全仓")
    print("=" * 90)
    print(
        f"最佳稳定参数: BB({int(best_stable['period'])},{best_stable['std']}) | "
        f"权益={best_stable['final_eq']:.1f}U | 收益={best_stable['ret_pct']:+.1f}% | "
        f"回撤={best_stable['max_dd_pct']:.1f}% | Sharpe={best_stable['sharpe']:.2f} | "
        f"交易={int(best_stable['n_trades'])}"
    )
    print(
        f"最高收益参数: BB({int(best_return['period'])},{best_return['std']}) | "
        f"权益={best_return['final_eq']:.1f}U | 收益={best_return['ret_pct']:+.1f}% | "
        f"回撤={best_return['max_dd_pct']:.1f}% | Sharpe={best_return['sharpe']:.2f} | "
        f"交易={int(best_return['n_trades'])}"
    )
    print("=" * 90)

    cfg = BBMidlineConfig(
        bb_period=int(best_stable["period"]),
        bb_std=float(best_stable["std"]),
        initial_capital=200.0,
        margin_pct=0.01,
        leverage=100.0,
        cross_margin=True,
        fee_rate=0.0005,
        rebate_pct=0.90,
        rebate_hour_utc=0,
        fill_at_close=True,
        use_1m_touch_filter=use_1m,
        kline_step_min=step_min,
    )
    final_res = run_bb_midline_backtest(
        df, cfg, df_1m=df_1m if use_1m else None
    )
    eq = final_res.equity_curve["equity"].dropna()

    print("\n逐年权益 (年末):")
    eq_ts = eq.copy()
    eq_ts.index = pd.to_datetime(eq_ts.index)
    prev = 200.0
    for y in range(2020, 2026):
        sub = eq_ts[eq_ts.index.year == y]
        if len(sub) > 0:
            ye = float(sub.iloc[-1])
            ret = (ye - prev) / prev * 100.0 if prev > 0 else 0.0
            print(f"  {y}: {ye:.1f} U (当年收益 {ret:+.1f}%)")
            prev = ye

    trade_path = out_dir / f"bb_midline_best_trades_{args.kline_period}_{stamp}.csv"
    rows = []
    for i, t in enumerate(final_res.trades, 1):
        rows.append({
            "序号": i,
            "方向": "做多" if t.side == "long" else "做空",
            "开仓时间": t.entry_time,
            "平仓时间": t.exit_time,
            "开仓价": round(t.entry_price, 2),
            "平仓价": round(t.exit_price, 2),
            "净盈亏": round(t.net_pnl, 2),
            "平仓原因": t.exit_reason,
        })
    pd.DataFrame(rows).to_csv(trade_path, index=False, encoding="utf-8-sig")
    print(f"\n最佳参数交易明细: {trade_path}")


if __name__ == "__main__":
    main()