"""回测 CLI 命令。"""

from __future__ import annotations

import importlib.util
from pathlib import Path
from typing import Any

import click


@click.command()
@click.argument("market")
@click.argument("code")
@click.option("--strategy", "strategy_str", default=None, help="DSL 策略表达式 (P1)")
@click.option("--strategy-file", "strategy_file", default=None, help="Python 策略文件路径")
@click.option(
    "--combo-strategies",
    "combo_strategies",
    default=None,
    help="多因子组合：逗号分隔的策略文件路径（如 strats/a.py,strats/b.py,strats/c.py）",
)
@click.option(
    "--combo-mode",
    "combo_mode",
    default="MAJORITY",
    type=click.Choice(["AND", "OR", "MAJORITY"], case_sensitive=False),
    help="多因子信号合并模式（默认 MAJORITY）",
)
@click.option("--cash", default=100000.0, type=float, help="初始资金")
@click.option("--commission", default=0.0003, type=float, help="佣金率")
@click.option(
    "--auto-fees",
    "auto_fees",
    is_flag=True,
    help="按标的品种自动解析费率（ETF/可转债免印花税等；显式 --commission 优先）",
)
@click.option(
    "--execution",
    default="next_open",
    type=click.Choice(["next_open", "next_close"]),
    help="成交价规则",
)
@click.option("--period", default="DAILY", help="K线周期")
@click.option("--adjust", default="NONE", help="复权: NONE/QFQ/HFQ")
@click.option("--count", default=500, type=int, help="K线数量")
@click.option("--indicators", default=None, help="预计算指标（逗号分隔）")
@click.option(
    "--chanlun-level",
    "chanlun_level",
    default=None,
    help="自动计算缠论分析并注入策略（如 DAILY/30MIN）",
)
@click.option("--table", "use_table", is_flag=True, help="表格输出")
@click.option("--output", "output_fmt", type=click.Choice(["json", "table", "csv"]), default="json")
@click.option(
    "--wf",
    "walk_forward",
    is_flag=True,
    help="附加 Walk-Forward 样本外验证（默认 7 窗，每窗独立开仓）",
)
@click.option("--wf-windows", "wf_windows", default=7, type=int, help="Walk-Forward 窗口数")
@click.option(
    "--evaluate",
    "full_evaluate",
    is_flag=True,
    help="一条龙评估：回测+WF+适配性+综合评分+S-D评级+买入持有基准对比（覆盖常规输出）",
)
def backtest(
    market: str,
    code: str,
    strategy_str: str | None,
    strategy_file: str | None,
    combo_strategies: str | None,
    combo_mode: str,
    cash: float,
    commission: float,
    auto_fees: bool,
    execution: str,
    period: str,
    adjust: str,
    count: int,
    indicators: str | None,
    chanlun_level: str | None,
    use_table: bool,
    output_fmt: str,
    walk_forward: bool,
    wf_windows: int,
    full_evaluate: bool,
) -> None:
    """回测引擎：执行策略并返回绩效报告。

    示例：

      easy-tdx backtest SZ 000001 --strategy-file my_strategy.py

      easy-tdx backtest SH 600519 --strategy-file ma_cross.py --table

      easy-tdx backtest SZ 000001 --strategy-file my_strategy.py --indicators MACD,KDJ

      easy-tdx backtest SZ 000001 --strategy-file chanlun_strategy.py --chanlun-level DAILY

      easy-tdx backtest SZ 300308 --strategy-file ma_cross.py --wf --wf-windows 7

      easy-tdx backtest SZ 300308 --strategy-file ma_cross.py --evaluate

      easy-tdx backtest SZ 000001 \
        --combo-strategies strategies/macd_cross.py,strategies/rsi_reversal.py \
        --combo-mode MAJORITY --table
    """
    from ..backtest.engine import BacktestEngine
    from ..cli.conn import get_mac_client
    from ..cli.parsers import parse_adjust, parse_market, parse_period
    from ..indicator import compute_indicators

    # 1. 加载策略（单策略 or 多因子组合）
    is_combo = combo_strategies is not None

    if is_combo:
        assert combo_strategies is not None  # narrowed by is_combo
        combo_classes = _load_combo_strategies(combo_strategies)
    else:
        strategy_cls = _load_strategy(strategy_str, strategy_file)
        if strategy_cls is None:
            click.echo("错误: 必须指定 --strategy-file / --combo-strategies / --strategy", err=True)
            raise SystemExit(1)

    # 2. 获取数据
    mkt = parse_market(market)
    with get_mac_client() as client:
        df = client.get_stock_kline(
            mkt,
            code,
            period=parse_period(period),
            start=0,
            count=count,
            adjust=parse_adjust(adjust),
        )

    # 3. 预计算指标
    if indicators:
        indicator_list = [ind.strip() for ind in indicators.split(",")]
        df = compute_indicators(df, indicator_list)

    # 4. 创建引擎并运行
    if is_combo:
        from ..backtest.combo import CombinationRunner

        runner = CombinationRunner(
            strategy_classes=combo_classes,
            df=df,
            cash=cash,
            commission=commission,
            execution=execution,
        )
        result = runner.run_combination(
            indices=list(range(len(combo_classes))),
            mode=combo_mode.upper(),
        )
    else:
        assert strategy_cls is not None  # guarded above by SystemExit

        # 一条龙评估：覆盖常规输出（含回测本身，无需重复跑）
        if full_evaluate:
            import json as _json

            from ..backtest.benchmark import evaluate_strategy

            report = evaluate_strategy(
                strategy=strategy_cls,
                df=df,
                cash=cash,
                commission=commission,
                execution=execution,
                symbol=f"{market}:{code}",
                auto_fees=auto_fees,
                n_windows=wf_windows,
            )
            click.echo(_json.dumps(report, ensure_ascii=False, default=str))
            return

        engine = BacktestEngine(
            strategy=strategy_cls,
            cash=cash,
            commission=commission,
            execution=execution,
            chanlun_level=chanlun_level,
            symbol=f"{market}:{code}",
            auto_fees=auto_fees,
        )
        result = engine.run(df)

    # 5. 输出结果
    fmt = "table" if use_table else output_fmt
    if fmt == "json":
        click.echo(result.to_json())
    elif fmt == "table":
        _print_table(result)
    else:
        click.echo(result.to_json())

    # 6. 附加 Walk-Forward 样本外验证（--wf）
    if walk_forward and not is_combo:
        import json as _json

        from ..backtest.walkforward import WalkForwardEngine

        assert strategy_cls is not None
        wf = WalkForwardEngine(
            strategy=strategy_cls,
            n_windows=wf_windows,
            cash=cash,
            commission=commission,
            execution=execution,
            symbol=f"{market}:{code}",
            auto_fees=auto_fees,
        )
        wf_report = {"walkforward": wf.run(df).to_dict()}
        click.echo(_json.dumps(wf_report, ensure_ascii=False, default=str))


def _load_strategy(strategy_str: str | None, strategy_file: str | None) -> type | None:
    """加载策略类。

    优先从 Python 文件加载，其次从 DSL 表达式加载（未实现）。

    Args:
        strategy_str: DSL 策略表达式
        strategy_file: Python 策略文件路径

    Returns:
        Strategy 子类
    """

    if strategy_file:
        return _load_strategy_from_file(strategy_file)

    if strategy_str:
        click.echo("错误: DSL 策略表达式尚未实现", err=True)
        return None

    return None


def _load_strategy_from_file(path: str) -> type:
    """从 Python 文件加载 Strategy 子类。

    Args:
        path: Python 文件路径

    Returns:
        Strategy 子类
    """
    from ..backtest.strategy import Strategy

    file_path = Path(path)
    if not file_path.exists():
        click.echo(f"错误: 文件不存在: {path}", err=True)
        raise SystemExit(1)

    spec = importlib.util.spec_from_file_location("strategy_module", file_path)
    if spec is None or spec.loader is None:
        click.echo(f"错误: 无法加载文件: {path}", err=True)
        raise SystemExit(1)

    module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(module)

    # 查找 Strategy 子类
    strategy_classes = []
    for name in dir(module):
        obj = getattr(module, name)
        try:
            if isinstance(obj, type) and issubclass(obj, Strategy) and obj is not Strategy:
                strategy_classes.append(obj)
        except TypeError:
            pass

    if not strategy_classes:
        click.echo(f"错误: 文件中未找到 Strategy 子类: {path}", err=True)
        raise SystemExit(1)

    if len(strategy_classes) > 1:
        click.echo(f"警告: 文件包含多个 Strategy 子类，使用第一个: {path}", err=True)

    return strategy_classes[0]


def _load_combo_strategies(combo_strategies: str) -> list[type]:
    """从逗号分隔的路径列表加载多个策略类。

    Args:
        combo_strategies: 逗号分隔的策略文件路径

    Returns:
        Strategy 子类列表
    """
    paths = [p.strip() for p in combo_strategies.split(",") if p.strip()]
    if len(paths) < 2:
        click.echo("错误: --combo-strategies 至少需要 2 个策略文件", err=True)
        raise SystemExit(1)

    classes: list[type] = []
    for p in paths:
        cls = _load_strategy_from_file(p)
        classes.append(cls)

    names = [c.__name__ for c in classes]
    click.echo(f"[*] 多因子组合 ({len(classes)} 因子): {' + '.join(names)}")
    return classes


def _print_table(result: Any) -> None:
    """以表格形式输出回测结果。"""
    perf = result.performance
    config = result.config

    click.echo("=== 回测绩效概要 ===")
    click.echo(f"总收益率: {perf.get('total_return', 0):.2%}")
    click.echo(f"年化收益: {perf.get('annual_return', 0):.2%}")
    click.echo(f"最大回撤: {perf.get('max_drawdown', 0):.2%}")
    click.echo(f"夏普比率: {perf.get('sharpe', 0):.2f}")
    click.echo(f"胜率: {perf.get('win_rate', 0):.2%}")
    click.echo(f"交易次数: {perf.get('total_trades', 0)}")
    # 深度风险指标（v1.28 新增；老结果缺键时跳过，不输出 0 假值）
    if perf.get("ulcer_index") is not None:
        click.echo(f"Ulcer 指数: {perf.get('ulcer_index', 0):.4f}")
        click.echo(f"日 VaR(95%): {perf.get('var_95', 0):.2%}")
        click.echo(f"日 CVaR(95%): {perf.get('cvar_95', 0):.2%}")
        click.echo(f"SQN 系统质量: {perf.get('sqn', 0):.2f}")
        click.echo(
            f"最大连胜/连亏: {perf.get('max_consecutive_wins', 0)} / "
            f"{perf.get('max_consecutive_losses', 0)}"
        )
    click.echo()

    if getattr(result, "diagnostic", None):
        click.echo(f"⚠ 诊断: {result.diagnostic}")
        click.echo()

    click.echo("=== 配置参数 ===")
    click.echo(f"初始资金: {config.get('cash', 0):.2f}")
    click.echo(f"佣金率: {config.get('commission', 0):.4f}")
    click.echo(f"成交规则: {config.get('execution', 'next_open')}")
    if config.get("chanlun_level"):
        click.echo(f"缠论级别: {config.get('chanlun_level')}")
    click.echo()

    if config.get("future_leak_warning"):
        click.echo("!!! 警告: 策略可能存在未来函数（使用未来数据）")
        click.echo()

    if not result.trades.empty:
        click.echo("=== 最近交易记录 ===")
        recent_trades = result.trades.tail(10)
        for idx, trade in recent_trades.iterrows():
            direction = "买入" if trade["direction"] == "BUY" else "卖出"
            status = "拒绝" if trade["rejected"] else "成交"
            click.echo(
                f"  [{trade['datetime']}] {direction} "
                f"数量={trade['size']:.0f} 价格={trade['price']:.2f} "
                f"盈亏={trade['pnl']:.2f} [{status}]"
            )
    else:
        click.echo("无交易记录")


# ── portfolio 多标的组合回测命令 ─────────────────────────────────────────────


@click.command()
@click.option(
    "--stocks",
    required=True,
    help="股票列表：逗号分隔的 市场:代码（如 SZ:000001,SH:600519,SH:600036）",
)
@click.option("--strategy-file", "strategy_file", required=True, help="Python 策略文件路径")
@click.option("--cash", default=200_000.0, type=float, help="总资金（默认 20 万）")
@click.option("--commission", default=0.0003, type=float, help="佣金率")
@click.option(
    "--execution",
    default="next_open",
    type=click.Choice(["next_open", "next_close"]),
    help="成交价规则",
)
@click.option("--period", default="DAILY", help="K线周期")
@click.option("--adjust", default="NONE", help="复权: NONE/QFQ/HFQ")
@click.option("--count", default=500, type=int, help="K线数量")
@click.option(
    "--allocation",
    default="equal",
    type=click.Choice(["equal"], case_sensitive=False),
    help="资金分配方式（默认 equal 均等分配）",
)
@click.option(
    "--chanlun-level",
    "chanlun_level",
    default=None,
    help="自动计算缠论分析并注入策略（如 DAILY/30MIN）",
)
@click.option(
    "--auto-fees",
    "auto_fees",
    is_flag=True,
    help="按标的品种自动解析费率（ETF/可转债免印花税等；显式 --commission 优先）",
)
@click.option("--wf", "walk_forward", is_flag=True, help="附加组合级 Walk-Forward 样本外验证")
@click.option("--wf-windows", "wf_windows", default=7, type=int, help="Walk-Forward 窗口数")
@click.option(
    "--evaluate",
    "full_evaluate",
    is_flag=True,
    help="组合级一条龙评估：组合回测+组合WF+适配性+综合评分+组合评级+等权买入持有基准对比（覆盖常规输出）",
)
@click.option("--table", "use_table", is_flag=True, help="表格输出")
@click.option("--output", "output_fmt", type=click.Choice(["json", "table", "csv"]), default="json")
def portfolio(
    stocks: str,
    strategy_file: str,
    cash: float,
    commission: float,
    execution: str,
    period: str,
    adjust: str,
    count: int,
    allocation: str,
    chanlun_level: str | None,
    auto_fees: bool,
    walk_forward: bool,
    wf_windows: int,
    full_evaluate: bool,
    use_table: bool,
    output_fmt: str,
) -> None:
    """多标的组合回测：共享资金池，独立产生信号，统一管理仓位。

    对多只股票同时回测，按均等比例分配资金，汇总组合整体绩效。

    示例：

      easy-tdx portfolio --stocks SZ:000001,SH:600519 --strategy-file ma_cross.py

      easy-tdx portfolio --stocks SZ:000001,SH:600519,SH:600036 \\
        --strategy-file my_strategy.py --cash 500000 --table

      easy-tdx portfolio --stocks SZ:000001,SH:600519 \\
        --strategy-file chanlun_strat.py --chanlun-level DAILY

      easy-tdx portfolio --stocks SZ:000001,SH:600519 \\
        --strategy-file ma_cross.py --wf --wf-windows 7

      easy-tdx portfolio --stocks SZ:000001,SH:600519 \\
        --strategy-file ma_cross.py --evaluate
    """
    import json

    from ..cli.conn import get_mac_client
    from ..cli.parsers import parse_adjust, parse_market, parse_period
    from .portfolio_engine import PortfolioBacktestEngine, StockData

    # 1. 加载策略
    strategy_cls = _load_strategy_from_file(strategy_file)
    strategy_name = strategy_cls.__name__

    # 2. 解析股票列表
    stock_list = []
    for item in stocks.split(","):
        item = item.strip()
        if ":" not in item:
            click.echo(f"错误: 股票格式应为 市场:代码，如 SZ:000001，收到: {item}", err=True)
            raise SystemExit(1)
        mkt_str, code = item.split(":", 1)
        stock_list.append((mkt_str.strip().upper(), code.strip()))

    if not stock_list:
        click.echo("错误: 未指定股票", err=True)
        raise SystemExit(1)

    click.echo(f"策略: {strategy_name} | 标的: {len(stock_list)} 只 | 资金: {cash:,.0f}", err=True)

    # 3. 获取数据
    stock_data_list: list[StockData] = []
    with get_mac_client() as client:
        for mkt_str, code in stock_list:
            mkt = parse_market(mkt_str)
            df = client.get_stock_kline(
                mkt,
                code,
                period=parse_period(period),
                start=0,
                count=count,
                adjust=parse_adjust(adjust),
            )
            stock_data_list.append(StockData(code=code, market=mkt_str, df=df))

    # 4. 组合级一条龙评估：覆盖常规输出（含组合回测本身，无需重复跑）
    if full_evaluate:
        from .benchmark import evaluate_portfolio

        report = evaluate_portfolio(
            strategy=strategy_cls,
            stocks=stock_data_list,
            total_cash=cash,
            commission=commission,
            execution=execution,
            chanlun_level=chanlun_level,
            auto_fees=auto_fees,
            n_windows=wf_windows,
        )
        click.echo(json.dumps(report, ensure_ascii=False, default=str))
        return

    # 5. 组合级 Walk-Forward 样本外验证（--wf）
    if walk_forward:
        from .walkforward import PortfolioWalkForwardEngine

        wf = PortfolioWalkForwardEngine(
            strategy=strategy_cls,
            stocks=stock_data_list,
            n_windows=wf_windows,
            total_cash=cash,
            commission=commission,
            execution=execution,
            chanlun_level=chanlun_level,
            auto_fees=auto_fees,
        )
        click.echo(json.dumps({"walkforward": wf.run().to_dict()}, ensure_ascii=False, default=str))
        return

    # 6. 常规组合回测
    engine = PortfolioBacktestEngine(
        strategy=strategy_cls,
        stocks=stock_data_list,
        total_cash=cash,
        allocation=allocation,
        commission=commission,
        execution=execution,
        chanlun_level=chanlun_level,
        auto_fees=auto_fees,
    )
    result = engine.run()

    # 7. 输出结果
    fmt = "table" if use_table else output_fmt
    if fmt == "table":
        _print_portfolio_table(result)
    else:
        click.echo(json.dumps(result.to_dict(), ensure_ascii=False, indent=2))


def _print_portfolio_table(result: Any) -> None:
    """以表格形式输出组合回测结果。"""
    perf = result.total_performance

    click.echo("=== 组合回测绩效概要 ===")
    click.echo(f"标的数量: {perf.get('total_stocks', 0)}")
    click.echo(f"总资金: {perf.get('total_cash', 0):,.0f}")
    click.echo(f"组合收益率: {perf.get('total_return', 0):.2%}")
    click.echo(f"组合年化: {perf.get('annual_return', 0):.2%}")
    click.echo()

    click.echo("── 各标的详情 ──")
    for key, stock_result in result.individual_results.items():
        sp = stock_result.performance
        alloc = result.equity_allocation.get(key, 0)
        click.echo(
            f"  {key}: 收益={sp.get('total_return', 0):.2%} "
            f"夏普={sp.get('sharpe', 0):.2f} "
            f"回撤={sp.get('max_drawdown', 0):.2%} "
            f"分配={alloc:.0%} "
            f"交易={sp.get('total_trades', 0)}"
        )
    click.echo()


# ── strategies 内置策略列表命令 ──────────────────────────────────────────────


@click.command("strategies")
@click.option("--output", "output_fmt", type=click.Choice(["json", "table"]), default="table")
def strategies(output_fmt: str) -> None:
    """列出内置策略注册表：名称、参数定义与预设寻优网格。

    策略名可直接用于 optimize --strategy / Web API /backtest/run 的 strategy 字段。

    示例：

      easy-tdx strategies

      easy-tdx strategies --output json
    """
    import json

    from .strategies import get_registry

    entries = get_registry().all()

    if output_fmt == "json":
        click.echo(json.dumps([e.to_schema() for e in entries], ensure_ascii=False, indent=2))
        return

    click.echo(f"=== 内置策略（{len(entries)} 个）===\n")
    for entry in entries:
        schema = entry.to_schema()
        params_desc = ", ".join(f"{p['name']}={p['default']}" for p in schema["params"])
        grid = schema.get("preset_grid") or {}
        points = 1
        for vals in grid.values():
            points *= len(vals)
        grid_desc = " × ".join(f"{k}:{len(v)}" for k, v in grid.items()) if grid else "无"
        click.echo(f"  {entry.name}  —  {entry.label}")
        click.echo(f"    参数: {params_desc or '无'}")
        click.echo(f"    预设网格: {grid_desc}（{points} 点）")
        if entry.description:
            click.echo(f"    说明: {entry.description}")
        click.echo()


# ── optimize 参数网格寻优命令 ────────────────────────────────────────────────


def _coerce_param_value(raw: str) -> Any:
    """把字符串参数值尽量转为 int/float，失败保留字符串。"""
    try:
        return int(raw)
    except ValueError:
        pass
    try:
        return float(raw)
    except ValueError:
        return raw


def _parse_param_grid(pairs: tuple[str, ...]) -> dict[str, list[Any]]:
    """解析 --param fast=5,10,15 形式的自定义网格。"""
    grid: dict[str, list[Any]] = {}
    for item in pairs:
        name, sep, raw = item.partition("=")
        values = [_coerce_param_value(v.strip()) for v in raw.split(",") if v.strip()]
        if not sep or not name.strip() or not values:
            click.echo(f"错误: --param 格式应为 参数名=值1,值2，收到: {item}", err=True)
            raise SystemExit(1)
        grid[name.strip()] = values
    return grid


@click.command()
@click.argument("market")
@click.argument("code")
@click.option(
    "--strategy",
    "strategy_name",
    default=None,
    help="注册表策略名（见 strategies 命令；网格取 --param 或该策略预设）",
)
@click.option(
    "--all",
    "optimize_all",
    is_flag=True,
    help="一键寻优所有内置策略：逐策略按预设网格寻优，输出全局排名",
)
@click.option(
    "--param",
    "param_pairs",
    multiple=True,
    help="自定义参数网格，如 --param fast=5,10,15 --param slow=20,60（覆盖预设）",
)
@click.option("--cash", default=1_000_000.0, type=float, help="初始资金")
@click.option("--commission", default=0.0003, type=float, help="佣金率")
@click.option("--slippage", default=0.0, type=float, help="滑点")
@click.option(
    "--execution",
    default="next_open",
    type=click.Choice(["next_open", "next_close"]),
    help="成交价规则",
)
@click.option(
    "--workers",
    default=1,
    type=int,
    help="并行进程数：1=串行+指标缓存（默认）；2+=进程级并行",
)
@click.option("--period", default="DAILY", help="K线周期")
@click.option("--adjust", default="NONE", help="复权: NONE/QFQ/HFQ")
@click.option("--count", default=500, type=int, help="K线数量")
@click.option("--top", default=15, type=int, help="表格输出显示前 N 行")
@click.option("--table", "use_table", is_flag=True, help="表格输出")
@click.option("--output", "output_fmt", type=click.Choice(["json", "table"]), default="json")
def optimize(
    market: str,
    code: str,
    strategy_name: str | None,
    optimize_all: bool,
    param_pairs: tuple[str, ...],
    cash: float,
    commission: float,
    slippage: float,
    execution: str,
    workers: int,
    period: str,
    adjust: str,
    count: int,
    top: int,
    use_table: bool,
    output_fmt: str,
) -> None:
    """参数网格寻优：单策略网格搜索，或 --all 一键寻优所有内置策略。

    示例：

      easy-tdx optimize SZ 000001 --strategy ma_cross

      easy-tdx optimize SZ 000001 --strategy ma_cross \\
        --param fast=5,10,15 --param slow=20,60

      easy-tdx optimize SZ 000001 --all --workers 4 --table
    """
    from ..cli.conn import get_mac_client
    from ..cli.parsers import parse_adjust, parse_market, parse_period
    from .strategies import get_registry
    from .strategies.presets import get_preset

    registry = get_registry()

    # 1. 校验模式与策略（联网取数之前，快速失败）
    if optimize_all == (strategy_name is not None):
        click.echo("错误: --all 与 --strategy 二选一", err=True)
        raise SystemExit(1)

    custom_grid = _parse_param_grid(param_pairs) if param_pairs else None
    if not optimize_all:
        assert strategy_name is not None
        try:
            entry = registry.get(strategy_name)
        except KeyError as exc:
            click.echo(f"错误: {exc}", err=True)
            raise SystemExit(1) from None
        if custom_grid is not None:
            declared = {p.name for p in entry.params}
            unknown = set(custom_grid) - declared
            if unknown:
                click.echo(
                    f"错误: 未知参数 {sorted(unknown)}，"
                    f"'{strategy_name}' 可用参数: {sorted(declared)}",
                    err=True,
                )
                raise SystemExit(1)
        else:
            custom_grid = get_preset(strategy_name)
            if not custom_grid:
                first_param = entry.params[0].name if entry.params else "参数名"
                click.echo(
                    f"错误: 策略 '{strategy_name}' 未登记预设网格，请用 --param 指定（如 "
                    f"--param {first_param}=5,10,20）",
                    err=True,
                )
                raise SystemExit(1)

    # 2. 获取数据
    mkt = parse_market(market)
    with get_mac_client() as client:
        df = client.get_stock_kline(
            mkt,
            code,
            period=parse_period(period),
            start=0,
            count=count,
            adjust=parse_adjust(adjust),
        )

    # 3. 寻优
    fmt = "table" if use_table else output_fmt
    import json

    if optimize_all:
        from .optimizer import optimize_all_strategies

        report = optimize_all_strategies(
            df,
            cash=cash,
            commission=commission,
            slippage=slippage,
            execution=execution,
            workers=workers,
        )
        if fmt == "table":
            _print_optimize_all_table(report, top)
        else:
            click.echo(json.dumps(report, ensure_ascii=False, default=str))
        return

    from .optimizer import ParamGridOptimizer

    assert strategy_name is not None and custom_grid is not None
    optimizer = ParamGridOptimizer(
        strategy_name=strategy_name,
        param_grid=custom_grid,
        df=df,
        cash=cash,
        commission=commission,
        slippage=slippage,
        execution=execution,
        workers=workers,
    )
    result = optimizer.run()
    if fmt == "table":
        _print_optimize_table(result.to_dict(), top)
    else:
        click.echo(json.dumps(result.to_dict(), ensure_ascii=False, default=str))


def _fmt_grid_point(params: dict[str, Any]) -> str:
    """把参数字典格式化为 fast=10, slow=20 形式。"""
    return ", ".join(f"{k}={v}" for k, v in params.items())


def _print_optimize_table(report: dict[str, Any], top: int) -> None:
    """以表格形式输出单策略寻优结果。"""
    results = report.get("results") or []
    best = report.get("best")
    click.echo(f"=== 参数寻优: {report.get('strategy')}（{len(results)} 个有效网格点）===\n")
    click.echo(
        f"{'排名':<4} {'参数':<28} {'总收益率':>8} {'夏普':>6} {'最大回撤':>8} "
        f"{'交易':>4} {'胜率':>6}"
    )
    for i, r in enumerate(results[:top], 1):
        click.echo(
            f"{i:<4} {_fmt_grid_point(r['params']):<28} {r['total_return']:>8.2%} "
            f"{r['sharpe']:>6.2f} {r['max_drawdown']:>8.2%} "
            f"{r['total_trades']:>4} {r['win_rate']:>6.1%}"
        )
    if best:
        click.echo(f"\n最佳参数: {_fmt_grid_point(best['params'])}")
    if len(results) > top:
        click.echo(f"（仅显示前 {top} 行，完整结果用 --output json）")


def _print_optimize_all_table(report: dict[str, Any], top: int) -> None:
    """以表格形式输出 --all 全策略寻优排名。"""
    ranking = report.get("ranking") or []
    click.echo(
        f"=== 一键寻优所有策略（{len(ranking)} 个策略，"
        f"共 {report.get('total_grid_points', 0)} 网格点）===\n"
    )
    click.echo(
        f"{'排名':<4} {'策略':<20} {'最佳参数':<28} {'总收益率':>8} {'夏普':>6} "
        f"{'最大回撤':>8} {'交易':>4} {'胜率':>6}"
    )
    for i, r in enumerate(ranking[:top], 1):
        label = f"{r['strategy']} {r.get('strategy_label', '')}"
        click.echo(
            f"{i:<4} {label:<20} {_fmt_grid_point(r['params']):<28} "
            f"{r['total_return']:>8.2%} {r['sharpe']:>6.2f} "
            f"{r['max_drawdown']:>8.2%} {r['total_trades']:>4} {r['win_rate']:>6.1%}"
        )
    if report.get("skipped"):
        click.echo(f"\n跳过（未注册）: {', '.join(report['skipped'])}")
    if ranking:
        click.echo(f"\n全局最优: {ranking[0]['strategy']} {_fmt_grid_point(ranking[0]['params'])}")
    if len(ranking) > top:
        click.echo(f"（仅显示前 {top} 行，完整结果用 --output json）")