feat: add 'run-all' CLI command for batch strategy backtesting (v1.9.3)

README.md

@@ -289,9 +289,24 @@ easy-tdx backtest SZ 300308 --strategy-file strategies/expma_cross.py --count 20
 交易次数: 24
 ```
 
-**全策略批量对比：**
+**全策略批量对比（CLI）：**
 
-项目自带 `run_all_strategies.py`，一次跑完 `strategies/` 下所有策略并排名：
+`easy-tdx run-all` 一行命令跑完 `strategies/` 下所有策略并排名：
+
+```bash
+easy-tdx run-all SZ 300308 --count 2000 --cash 1000000 --adjust QFQ
+
+# 多因子组合回测
+easy-tdx run-all SZ 300308 --combo 2 --combo-mode MAJORITY
+
+# 加 --show 自动弹出最佳策略的资金曲线 vs 股价对比图
+easy-tdx run-all SZ 300308 --count 2000 --cash 1000000 --adjust QFQ --show
+
+# 自定义策略目录
+easy-tdx run-all SZ 300308 --strategies-dir my_strategies/
+```
+
+也可使用项目自带的 `run_all_strategies.py` 脚本（功能相同）：
 
 ```bash
 python -X utf8 run_all_strategies.py SZ 300308 --count 2000 --cash 1000000 --adjust QFQ
@@ -308,8 +323,8 @@ python -X utf8 run_all_strategies.py SZ 300308 --count 2000 --cash 1000000 --adj
 # 自动寻找最佳 2 因子和 3 因子组合（MAJORITY 模式）
 python -X utf8 run_all_strategies.py SZ 300308 --combo 2 --combo 3 --combo-mode majority
 
-# 也可用 AND / OR 模式
-python -X utf8 run_all_strategies.py SZ 300308 --combo 2 --combo-mode and
+# CLI 方式
+easy-tdx run-all SZ 300308 --combo 2 --combo 3 --combo-mode majority
 ```
 
 CLI 指定策略文件组合：
@@ -679,6 +694,7 @@ easy-tdx offline sync-all
 | `indicator` | 技术指标计算（32 个：MACD/KDJ/RSI/BOLL/DMI/ATR...） |
 | `indicator-list` | 列出可用技术指标 |
 | `backtest` | 回测引擎（加载策略文件，输出绩效报告） |
+| `run-all` | 批量运行所有策略并排名（绩效排名 + 综合评分 + 可选图表） |
 | `screen scan` | 策略选股扫描（纯离线，全市场信号扫描） |
 | `screen rank` | 扫描结果回测排名（按夏普/回撤等指标排序） |
 | `f10` | F10 公司信息 |
@@ -1230,6 +1246,16 @@ ruff format --check src/ tests/                              # format check
 
 ## Changelog
 
+### 1.9.3 (2026-06-10)
+
+**新增 `run-all` CLI 命令** — 一行命令批量运行 strategies/ 目录下所有策略并排名，与 `run_all_strategies.py` 脚本功能完全一致。
+
+- 新增 `easy-tdx run-all` CLI 命令，支持 `--count`、`--cash`、`--commission`、`--adjust`、`--period`、`--combo`、`--combo-mode`、`--show`、`--strategies-dir` 参数
+- 绩效排名 + 综合评分 + 最佳策略交易明细，输出与脚本完全一致
+- 支持多因子组合回测（`--combo 2 --combo 3`）和资金曲线图表展示（`--show`）
+- 支持自定义策略目录（`--strategies-dir`）
+- `run_all_strategies.py` 保持不变，两种方式并存
+
 ### 1.9.2 (2026-06-10)
 
 **策略选股扫描器** — 新增 `screen` 命令组，用策略扫描全市场找出触发买入信号的股票，再做历史回测排名。纯离线数据，零网络 IO。

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "easy-tdx"
-version = "1.9.2"
+version = "1.9.3"
 description = "通达信 TCP 协议行情数据客户端，支持在线行情、离线数据读取与写入同步"
 readme = "README.md"
 requires-python = ">=3.10"

src/easy_tdx/cli/__init__.py

@@ -19,6 +19,7 @@ from .cmd_kline import kline
 from .cmd_monitor import market_stat, unusual
 from .cmd_offline import offline
 from .cmd_quote import quote, quote_list
+from .cmd_run_all import run_all
 from .cmd_tick import tick
 from .cmd_transaction import transaction
 
@@ -72,4 +73,5 @@ cli.add_command(indicator_list)
 cli.add_command(offline)
 cli.add_command(chanlun)
 cli.add_command(backtest)
+cli.add_command(run_all)
 cli.add_command(screen)

src/easy_tdx/cli/cmd_run_all.py

@@ -0,0 +1,556 @@
+"""run-all 命令 — 批量运行 strategies/ 目录下所有策略并比较结果。
+
+用法::
+
+    easy-tdx run-all SZ 300308 --count 2000 --cash 1000000 --adjust QFQ
+
+输出每个策略的绩效指标，并按总收益率排名。
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import time
+from pathlib import Path
+from typing import Any
+
+import click
+
+# ── 辅助函数 ──────────────────────────────────────────────────────────────────
+
+
+def _load_strategy_class(file_path: Path) -> type | None:
+    """从 Python 文件加载 Strategy 子类，失败返回 None。"""
+    from ..backtest.strategy import Strategy
+
+    spec = importlib.util.spec_from_file_location("strategy_module", file_path)
+    if spec is None or spec.loader is None:
+        return None
+
+    module = importlib.util.module_from_spec(spec)
+    try:
+        spec.loader.exec_module(module)
+    except Exception:
+        return None
+
+    for attr_name in dir(module):
+        obj = getattr(module, attr_name)
+        try:
+            if isinstance(obj, type) and issubclass(obj, Strategy) and obj is not Strategy:
+                return obj
+        except TypeError:
+            pass
+
+    return None
+
+
+def _setup_chinese_font() -> None:
+    """配置 matplotlib 中文字体，按平台自动选择。"""
+    import platform
+
+    import matplotlib
+
+    system = platform.system()
+    if system == "Windows":
+        candidates = ["Microsoft YaHei", "SimHei", "KaiTi", "FangSong"]
+    elif system == "Darwin":
+        candidates = ["PingFang SC", "Heiti SC", "STHeiti"]
+    else:
+        candidates = ["WenQuanYi Micro Hei", "Noto Sans CJK SC", "Droid Sans Fallback"]
+
+    import matplotlib.font_manager as fm
+
+    available = {f.name for f in fm.fontManager.ttflist}
+    for font in candidates:
+        if font in available:
+            matplotlib.rcParams["font.sans-serif"] = [font, "DejaVu Sans"]
+            break
+    matplotlib.rcParams["axes.unicode_minus"] = False
+
+
+def _map_trade_values(trades_df: Any, equity: Any, initial_cash: float) -> list[float]:
+    """将交易的 datetime 映射到 equity_curve 对应的归一化值。"""
+    eq_dt = equity["datetime"].values
+    eq_norm = equity["total"].values / initial_cash
+    result_vals: list[float] = []
+    for dt in trades_df["datetime"].values:
+        idx = eq_dt.searchsorted(dt, side="right") - 1
+        if idx < 0:
+            idx = 0
+        if idx >= len(eq_norm):
+            idx = len(eq_norm) - 1
+        result_vals.append(float(eq_norm[idx]))
+    return result_vals
+
+
+def _print_ranking(
+    results: list[dict[str, Any]],
+    backtest_results: dict[str, Any],
+) -> bool:
+    """输出策略绩效排名、综合评分和最佳策略明细。
+
+    Returns:
+        True 表示有有效结果，False 表示全部失败。
+    """
+    valid = [r for r in results if "error" not in r]
+    errored = [r for r in results if "error" in r]
+
+    if not valid:
+        click.echo("所有策略均运行失败！")
+        for r in errored:
+            click.echo(f"  {r['strategy']}: {r['error']}")
+        return False
+
+    valid.sort(key=lambda x: x["total_return"], reverse=True)
+
+    # ── 绩效排名 ──────────────────────────────────────────────────────────────
+    click.echo("\n" + "=" * 80)
+    click.echo("[*] 策略绩效排名 (按总收益率降序)")
+    click.echo("=" * 80)
+
+    click.echo(
+        f"{'排名':>4}  {'策略':<22} {'总收益率':>10} {'年化收益':>10} "
+        f"{'最大回撤':>10} {'夏普':>8} {'胜率':>8} {'交易次数':>8} {'盈亏比':>8}"
+    )
+    click.echo("-" * 100)
+
+    for i, r in enumerate(valid, 1):
+        medal = " *1*" if i == 1 else " *2*" if i == 2 else " *3*" if i == 3 else "    "
+        click.echo(
+            f"{medal}{i:>2}  {r['strategy']:<22} "
+            f"{r['total_return']:>9.2%} "
+            f"{r['annual_return']:>9.2%} "
+            f"{r['max_drawdown']:>9.2%} "
+            f"{r['sharpe']:>8.2f} "
+            f"{r['win_rate']:>7.1%} "
+            f"{r['total_trades']:>8} "
+            f"{r['profit_factor']:>8.2f}"
+        )
+
+    # ── 最佳策略详细报告 ──────────────────────────────────────────────────────
+    best = valid[0]
+    click.echo("\n" + "=" * 80)
+    click.echo(f"[BEST] 最佳策略: {best['strategy']}")
+    click.echo("=" * 80)
+    click.echo(f"  总收益率:   {best['total_return']:.2%}")
+    click.echo(f"  年化收益:   {best['annual_return']:.2%}")
+    click.echo(f"  最大回撤:   {best['max_drawdown']:.2%}")
+    click.echo(f"  夏普比率:   {best['sharpe']:.2f}")
+    click.echo(f"  索提诺:     {best['sortino']:.2f}")
+    click.echo(f"  卡玛比率:   {best['calmar']:.2f}")
+    click.echo(f"  胜率:       {best['win_rate']:.1%}")
+    click.echo(f"  交易次数:   {best['total_trades']}")
+    click.echo(f"  盈亏比:     {best['profit_factor']:.2f}")
+    click.echo(f"  年化波动:   {best['volatility']:.4f}")
+
+    # ── 综合评分 ──────────────────────────────────────────────────────────────
+    click.echo("\n" + "=" * 80)
+    click.echo("[*] 综合评分排名 (Sharpe*0.4 + Ret/DD*0.3 + WinRate*0.3)")
+    click.echo("=" * 80)
+
+    scored: list[tuple[dict[str, Any], float]] = []
+    for r in valid:
+        ret_dd_ratio = r["annual_return"] / r["max_drawdown"] if r["max_drawdown"] > 1e-6 else 999.0
+        score = r["sharpe"] * 0.4 + ret_dd_ratio * 0.3 + r["win_rate"] * 100 * 0.3
+        scored.append((r, score))
+
+    scored.sort(key=lambda x: x[1], reverse=True)
+
+    click.echo(
+        f"{'排名':>4}  {'策略':<22} {'综合评分':>10} {'夏普':>8} {'收益/回撤':>10} {'胜率':>8}"
+    )
+    click.echo("-" * 70)
+    for i, (r, score) in enumerate(scored, 1):
+        ret_dd_ratio = r["annual_return"] / r["max_drawdown"] if r["max_drawdown"] > 1e-6 else 999.0
+        medal = " *1*" if i == 1 else " *2*" if i == 2 else " *3*" if i == 3 else "    "
+        click.echo(
+            f"{medal}{i:>2}  {r['strategy']:<22} {score:>10.2f} "
+            f"{r['sharpe']:>8.2f} {ret_dd_ratio:>10.2f} {r['win_rate']:>7.1%}"
+        )
+
+    # ── 最佳策略交易明细 ──────────────────────────────────────────────────────
+    best_name = valid[0]["strategy"]
+    if best_name in backtest_results:
+        bt = backtest_results[best_name]
+        bp = bt.performance
+        bc = bt.config
+
+        click.echo("\n" + "=" * 80)
+        click.echo(f"[DETAIL] 最佳策略交易明细: {best_name}")
+        click.echo("=" * 80)
+
+        click.echo("=== 回测绩效概要 ===")
+        click.echo(f"总收益率: {bp.get('total_return', 0):.2%}")
+        click.echo(f"年化收益: {bp.get('annual_return', 0):.2%}")
+        click.echo(f"最大回撤: {bp.get('max_drawdown', 0):.2%}")
+        click.echo(f"夏普比率: {bp.get('sharpe', 0):.2f}")
+        click.echo(f"胜率: {bp.get('win_rate', 0):.2%}")
+        click.echo(f"交易次数: {bp.get('total_trades', 0)}")
+        click.echo()
+        click.echo("=== 配置参数 ===")
+        click.echo(f"初始资金: {bc.get('cash', 0):.2f}")
+        click.echo(f"佣金率: {bc.get('commission', 0):.4f}")
+        click.echo(f"成交规则: {bc.get('execution', 'next_open')}")
+        click.echo()
+
+        if not bt.trades.empty:
+            click.echo("=== 最近交易记录 ===")
+            recent_trades = bt.trades.tail(10)
+            for _, 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")
+
+    # ── 报告错误 ──────────────────────────────────────────────────────────────
+    if errored:
+        click.echo("\n[!] 以下策略运行失败:")
+        for r in errored:
+            click.echo(f"  {r['strategy']}: {r['error']}")
+
+    return True
+
+
+def _run_combo_screen(
+    strategy_classes: dict[str, type],
+    df: Any,
+    cash: float,
+    commission: float,
+    combo_sizes: tuple[int, ...],
+    combo_mode: str,
+) -> None:
+    """运行多因子组合回测并输出排名。"""
+    from math import comb
+
+    from ..backtest.combo import CombinationRunner
+
+    classes_list = list(strategy_classes.values())
+    if len(classes_list) < 2:
+        click.echo("[!] 策略数量不足 2 个，跳过组合回测")
+        return
+
+    # 单个 Runner 跨 size 复用信号缓存，避免重复提取
+    runner = CombinationRunner(
+        strategy_classes=classes_list,
+        df=df,
+        cash=cash,
+        commission=commission,
+    )
+
+    for size in combo_sizes:
+        total = comb(len(classes_list), size)
+        click.echo("\n" + "=" * 80)
+        click.echo(f"[*] {size}因子组合回测 (共{total}组, 模式={combo_mode})")
+        click.echo("=" * 80)
+
+        results = runner.screen(combo_sizes=(size,), mode=combo_mode.upper())
+
+        if not results:
+            click.echo("  无有效交易组合（所有组合均为零交易）")
+            continue
+
+        click.echo(
+            f"{'排名':>4}  {'因子组合':<50} {'总收益率':>10} {'年化收益':>10} "
+            f"{'最大回撤':>10} {'夏普':>8} {'胜率':>8} {'交易':>6}"
+        )
+        click.echo("-" * 120)
+
+        for i, r in enumerate(results[:20], 1):
+            medal = " *1*" if i == 1 else " *2*" if i == 2 else " *3*" if i == 3 else "    "
+            perf = r.result.performance
+            click.echo(
+                f"{medal}{i:>2}  {r.name:<50} "
+                f"{perf.get('total_return', 0):>9.2%} "
+                f"{perf.get('annual_return', 0):>9.2%} "
+                f"{perf.get('max_drawdown', 0):>9.2%} "
+                f"{perf.get('sharpe', 0):>8.2f} "
+                f"{perf.get('win_rate', 0):>7.1%} "
+                f"{perf.get('total_trades', 0):>6}"
+            )
+
+        if len(results) > 20:
+            click.echo(f"  ... 共 {len(results)} 个有效组合，仅显示前 20")
+
+        # 最佳组合详细报告
+        best = results[0]
+        bp = best.result.performance
+        click.echo(f"\n[BEST {size}因子] {best.name}")
+        click.echo(f"  总收益率: {bp.get('total_return', 0):.2%}")
+        click.echo(f"  年化收益: {bp.get('annual_return', 0):.2%}")
+        click.echo(f"  最大回撤: {bp.get('max_drawdown', 0):.2%}")
+        click.echo(f"  夏普比率: {bp.get('sharpe', 0):.2f}")
+        click.echo(f"  胜率:     {bp.get('win_rate', 0):.1%}")
+
+
+def _show_best_chart(
+    df: Any,
+    result: Any,
+    strategy_name: str,
+    stock_label: str,
+    stock_name: str,
+    initial_cash: float,
+) -> None:
+    """展示最佳策略资金曲线与股价归一化对比图。"""
+    try:
+        import matplotlib.pyplot as plt
+    except ImportError:
+        click.echo("[!] 需要 matplotlib 才能展示图表: pip install matplotlib")
+        return
+
+    _setup_chinese_font()
+
+    equity = result.equity_curve
+    if equity.empty:
+        click.echo("[!] 最佳策略无资金曲线数据，跳过绘图")
+        return
+
+    fig, ax1 = plt.subplots(figsize=(14, 7))
+
+    # 归一化股价（以第一天收盘价为基准）
+    close_prices = df["close"].values
+    norm_price = close_prices / close_prices[0]
+    dates = df["datetime"] if "datetime" in df.columns else df.index
+    ax1.plot(dates, norm_price, color="steelblue", linewidth=1.2, label="股价 (归一化)")
+    ax1.set_ylabel("股价归一化", color="steelblue", fontsize=11)
+    ax1.tick_params(axis="y", labelcolor="steelblue")
+
+    # 归一化资金曲线（以初始资金为基准）
+    eq_dates = equity["datetime"]
+    eq_values = equity["total"].values / initial_cash
+    ax2 = ax1.twinx()
+    ax2.plot(eq_dates, eq_values, color="crimson", linewidth=1.5, label=f"策略: {strategy_name}")
+    ax2.set_ylabel("资金曲线 (归一化)", color="crimson", fontsize=11)
+    ax2.tick_params(axis="y", labelcolor="crimson")
+
+    # 标记买卖点
+    trades = result.trades
+    if not trades.empty:
+        buy_trades = trades[trades["direction"] == "BUY"]
+        sell_trades = trades[trades["direction"] == "SELL"]
+        if not buy_trades.empty:
+            ax2.scatter(
+                buy_trades["datetime"].values,
+                _map_trade_values(buy_trades, equity, initial_cash),
+                marker="^",
+                color="green",
+                s=30,
+                alpha=0.7,
+                zorder=5,
+                label="买入",
+            )
+        if not sell_trades.empty:
+            ax2.scatter(
+                sell_trades["datetime"].values,
+                _map_trade_values(sell_trades, equity, initial_cash),
+                marker="v",
+                color="orange",
+                s=30,
+                alpha=0.7,
+                zorder=5,
+                label="卖出",
+            )
+
+    # 标题：股票代码 + 名称 + 策略绩效
+    title = f"{stock_label}"
+    if stock_name:
+        title += f" {stock_name}"
+    perf = result.performance
+    ret_str = f"{perf.get('total_return', 0):.1%}"
+    dd_str = f"{perf.get('max_drawdown', 0):.1%}"
+    sharpe_str = f"{perf.get('sharpe', 0):.2f}"
+    title += f"  |  最佳策略: {strategy_name}  |  收益 {ret_str}  回撤 {dd_str}  夏普 {sharpe_str}"
+
+    ax1.set_title(title, fontsize=12, pad=15)
+    ax1.set_xlabel("日期", fontsize=11)
+
+    # 合并两个轴的图例
+    lines1, labels1 = ax1.get_legend_handles_labels()
+    lines2, labels2 = ax2.get_legend_handles_labels()
+    ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left", fontsize=9)
+
+    fig.autofmt_xdate()
+    plt.tight_layout()
+    click.echo("\n正在显示图表，关闭窗口后继续...")
+    plt.show()
+
+
+# ── 主命令 ──────────────────────────────────────────────────────────────────
+
+
+@click.command("run-all")
+@click.argument("market")
+@click.argument("code")
+@click.option("--count", default=2000, type=int, help="K线数量")
+@click.option("--cash", default=1000000.0, type=float, help="初始资金")
+@click.option("--commission", default=0.0003, type=float, help="佣金率")
+@click.option("--adjust", default="QFQ", help="复权: NONE/QFQ/HFQ")
+@click.option("--period", default="DAILY", help="K线周期")
+@click.option(
+    "--combo",
+    "combo_sizes",
+    multiple=True,
+    type=int,
+    help="多因子组合回测（可多次指定，如 --combo 2 --combo 3）",
+)
+@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("--show", "show_chart", is_flag=True, help="显示最佳策略资金曲线 vs 股价对比图")
+@click.option(
+    "--strategies-dir",
+    "strategies_dir",
+    default="strategies",
+    help="策略文件目录（默认 strategies/）",
+)
+def run_all(
+    market: str,
+    code: str,
+    count: int,
+    cash: float,
+    commission: float,
+    adjust: str,
+    period: str,
+    combo_sizes: tuple[int, ...],
+    combo_mode: str,
+    show_chart: bool,
+    strategies_dir: str,
+) -> None:
+    """批量运行 strategies/ 目录下所有策略并比较结果。
+
+    依次运行指定目录下所有策略文件，输出绩效排名和综合评分。
+
+    示例：
+
+      easy-tdx run-all SZ 300308 --count 2000 --cash 1000000 --adjust QFQ
+
+      easy-tdx run-all SZ 300308 --combo 2 --combo-mode MAJORITY
+
+      easy-tdx run-all SZ 300308 --show
+    """
+    from ..backtest.engine import BacktestEngine
+    from ..cli.conn import get_mac_client
+    from ..cli.parsers import parse_adjust, parse_market, parse_period
+
+    # 1. 发现策略文件
+    sdir = Path(strategies_dir)
+    strategy_files = sorted(sdir.glob("*.py"))
+    if not strategy_files:
+        click.echo(f"未找到策略文件 ({strategies_dir}/*.py)", err=True)
+        raise SystemExit(1)
+
+    click.echo(f"发现 {len(strategy_files)} 个策略文件")
+    click.echo(f"标的: {market} {code} | K线: {count} | 资金: {cash:,.0f} | 复权: {adjust}")
+    click.echo("=" * 80)
+
+    # 2. 获取数据（所有策略共享同一份数据）
+    mkt = parse_market(market)
+    click.echo("正在获取行情数据...")
+    stock_name = ""
+    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),
+        )
+        # 获取股票名称（仅图表模式需要）
+        if show_chart:
+            try:
+                quotes_df = client.get_stock_quotes([(mkt, code)])
+                if not quotes_df.empty and "name" in quotes_df.columns:
+                    stock_name = str(quotes_df.iloc[0]["name"])
+            except Exception:
+                pass
+    click.echo(f"获取到 {len(df)} 条K线数据")
+    click.echo("=" * 80)
+
+    # 3. 逐个运行策略
+    results: list[dict[str, Any]] = []
+    backtest_results: dict[str, Any] = {}
+    strategy_classes: dict[str, type] = {}
+
+    for sf in strategy_files:
+        strategy_name = sf.stem
+        click.echo(f"\n>> 运行策略: {strategy_name} ...", nl=False)
+
+        strategy_cls = _load_strategy_class(sf)
+        if strategy_cls is None:
+            click.echo(" [加载失败/无 Strategy 子类]")
+            continue
+
+        strategy_classes[strategy_name] = strategy_cls
+        t0 = time.perf_counter()
+        try:
+            engine = BacktestEngine(
+                strategy=strategy_cls,
+                cash=cash,
+                commission=commission,
+            )
+            result = engine.run(df)
+            elapsed = time.perf_counter() - t0
+            perf = result.performance
+            click.echo(f" 完成 ({elapsed:.1f}s)")
+
+            results.append(
+                {
+                    "strategy": strategy_name,
+                    "total_return": perf.get("total_return", 0),
+                    "annual_return": perf.get("annual_return", 0),
+                    "max_drawdown": perf.get("max_drawdown", 0),
+                    "sharpe": perf.get("sharpe", 0),
+                    "sortino": perf.get("sortino", 0),
+                    "calmar": perf.get("calmar", 0),
+                    "win_rate": perf.get("win_rate", 0),
+                    "total_trades": perf.get("total_trades", 0),
+                    "profit_factor": perf.get("profit_factor", 0),
+                    "volatility": perf.get("volatility", 0),
+                }
+            )
+            backtest_results[strategy_name] = result
+        except Exception as e:
+            elapsed = time.perf_counter() - t0
+            click.echo(f" 错误 ({elapsed:.1f}s): {e}")
+            results.append({"strategy": strategy_name, "error": str(e)})
+
+    # 4. 输出排名
+    has_valid = _print_ranking(results, backtest_results)
+    if not has_valid:
+        raise SystemExit(1)
+
+    # 5. 多因子组合回测
+    if combo_sizes:
+        _run_combo_screen(
+            strategy_classes=strategy_classes,
+            df=df,
+            cash=cash,
+            commission=commission,
+            combo_sizes=combo_sizes,
+            combo_mode=combo_mode,
+        )
+
+    # 6. 展示最佳策略曲线图
+    if show_chart:
+        valid = [r for r in results if "error" not in r]
+        if valid:
+            valid.sort(key=lambda x: x["total_return"], reverse=True)
+            best_name = valid[0]["strategy"]
+            if best_name in backtest_results:
+                _show_best_chart(
+                    df=df,
+                    result=backtest_results[best_name],
+                    strategy_name=best_name,
+                    stock_label=f"{market}{code}",
+                    stock_name=stock_name,
+                    initial_cash=cash,
+                )