feat(backtest): add --show flag for best strategy equity curve chart

- Add --show/--show-chart flag to run_all_strategies.py
- Display dual-axis chart: normalized stock price vs strategy equity curve
- Mark buy/sell points with green/orange triangles
- Auto-detect Chinese fonts (SimHei/YaHei on Windows, PingFang on macOS)
- Fetch stock name via get_stock_quotes for chart title
- Add 3 demo screenshots to README with disclaimer
- Update README with --show usage and visual examples

README.md

@@ -295,8 +295,13 @@ easy-tdx backtest SZ 300308 --strategy-file strategies/expma_cross.py --count 20
 
 ```bash
 python -X utf8 run_all_strategies.py SZ 300308 --count 2000 --cash 1000000 --adjust QFQ
+
+# 加 --show 自动弹出最佳策略的资金曲线 vs 股价对比图
+python -X utf8 run_all_strategies.py SZ 300308 --count 2000 --cash 1000000 --adjust QFQ --show
 ```
 
+`--show` 会用 matplotlib 弹出一个双轴对比窗口：左轴蓝色线是归一化股价，右轴红色线是最佳策略的资金曲线，绿三角=买入、黄三角=卖出，标题显示股票名称和关键绩效指标。需要 `pip install matplotlib`。
+
 输出示例（以 SZ 300308 为例）：
 
 ```
@@ -345,6 +350,25 @@ python -X utf8 run_all_strategies.py SZ 300308 --count 2000 --cash 1000000 --adj
 python -X utf8 run_all_strategies.py SH 600519 --count 2000 --cash 1000000 --adjust QFQ
 ```
 
+#### `--show` 可视化效果
+
+<p align="center">
+  <img src="strategies/demo/1.png" width="700"><br>
+  <sub>SH601088 中国神华 — bollinger_breakout 策略 | 收益 1281.8%</sub>
+</p>
+
+<p align="center">
+  <img src="strategies/demo/2.png" width="700"><br>
+  <sub>SH600522 中天科技 — kdj_golden 策略 | 收益 568.6%</sub>
+</p>
+
+<p align="center">
+  <img src="strategies/demo/3.png" width="700"><br>
+  <sub>SH601179 中国西电 — expma_cross 策略 | 收益 168.0%</sub>
+</p>
+
+> **⚠️ Demo 展示，不作为操作依据。** 历史回测收益不代表未来表现，策略参数未经过样本外验证。
+
 #### 自带策略示例
 
 `strategies/` 目录下有 9 个开箱即用的策略文件，可直接用于 `--strategy-file`：

run_all_strategies.py

@@ -9,7 +9,6 @@
 
 from __future__ import annotations
 
-import json
 import sys
 import time
 from pathlib import Path
@@ -21,6 +20,128 @@ sys.path.insert(0, str(Path(__file__).parent / "src"))
 import click
 
 
+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 _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()
+
+
 @click.command()
 @click.argument("market")
 @click.argument("code")
@@ -29,6 +150,7 @@ import click
 @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("--show", "show_chart", is_flag=True, help="显示最佳策略资金曲线 vs 股价对比图")
 def run_all(
     market: str,
     code: str,
@@ -37,6 +159,7 @@ def run_all(
     commission: float,
     adjust: str,
     period: str,
+    show_chart: bool,
 ) -> None:
     """批量运行 strategies/ 目录下所有策略并比较结果。"""
     from easy_tdx.backtest.engine import BacktestEngine
@@ -60,6 +183,7 @@ def run_all(
     click.echo("正在获取行情数据...")
     client = MacClient.from_best_host()
     client.connect()
+    stock_name = ""
     try:
         df = client.get_stock_kline(
             mkt,
@@ -69,6 +193,14 @@ def run_all(
             count=count,
             adjust=parse_adjust(adjust),
         )
+        # 获取股票名称
+        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
     finally:
         client.close()
     click.echo(f"获取到 {len(df)} 条K线数据")
@@ -268,6 +400,19 @@ def run_all(
         for r in errored:
             click.echo(f"  {r['strategy']}: {r['error']}")
 
+    # 5. 展示最佳策略曲线图
+    if show_chart and valid:
+        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,
+            )
+
 
 if __name__ == "__main__":
     run_all()