feat(report): 新增月度收益矩阵热力图面板

在策略绩效对比表下方新增 Panel 2 月度收益矩阵：
- 行：年份（2020~2026），列：1月~12月 + 年度累计
- 单元格显示月度收益率百分比
- 红涨绿跌配色（A股习惯），sqrt非线性映射增强小收益可见性
- 年度列使用几何连乘计算全年累计收益
- 无数据单元格浅灰显示

rotation/simple_rotation.py

@@ -1312,8 +1312,8 @@ class SimpleRotationStrategy:
 
         n_rows = len(positions_info) + len(exit_positions) + len(unselected_positions)
         signal_h = max(1.5, 0.5 + n_rows * 0.35)
-        fig = plt.figure(figsize=(16, 10 + signal_h + 1.2 + 8))
-        gs = fig.add_gridspec(5, 1, height_ratios=[signal_h, 1.2, 3, 1, 1.2], hspace=0.35)
+        fig = plt.figure(figsize=(16, 10 + signal_h + 1.2 + 1.5 + 8))
+        gs = fig.add_gridspec(6, 1, height_ratios=[signal_h, 1.2, 1.5, 3, 1, 1.2], hspace=0.35)
 
         # Panel 0: Full asset ranking table
         ax0 = fig.add_subplot(gs[0])
@@ -1394,51 +1394,123 @@ class SimpleRotationStrategy:
             ptbl[1, j].set_facecolor("#d4edda")
             ptbl[2, j].set_facecolor("#cce5ff")
 
-        # Panel 2: NAV curves
+        # Panel 2: Monthly returns heatmap (year × month matrix)
         ax2 = fig.add_subplot(gs[2])
-        ax2.plot(strategy_nav.index, strategy_nav.values,
+        ax2.axis('off')
+        ax2.set_title('月度收益矩阵 (%)', fontsize=14, fontweight='bold', loc='left', pad=10)
+        # Resample to monthly NAV (last business day)
+        monthly_nav = strategy_nav.resample('ME').last().dropna()
+        monthly_ret = monthly_nav.pct_change().dropna()
+        # Build year-month matrix
+        monthly_ret_pct = (monthly_ret * 100).round(2)
+        year_month_df = pd.DataFrame({
+            'year': monthly_ret_pct.index.year,
+            'month': monthly_ret_pct.index.month,
+            'ret': monthly_ret_pct.values,
+        })
+        years = sorted(year_month_df['year'].unique())
+        month_labels = [f'{m}月' for m in range(1, 13)]
+        col_labels = month_labels + ['年度']
+        # Build table data
+        table_data = []
+        for yr in years:
+            row = []
+            yr_total = 0.0
+            yr_count = 0
+            for m in range(1, 13):
+                mask = (year_month_df['year'] == yr) & (year_month_df['month'] == m)
+                vals = year_month_df.loc[mask, 'ret'].values
+                if len(vals) > 0:
+                    v = float(vals[0])
+                    row.append(v)
+                    yr_total = (1 + yr_total / 100) * (1 + v / 100) * 100 - 100
+                    yr_count += 1
+                else:
+                    row.append(None)
+            row.append(round(yr_total, 2) if yr_count > 0 else None)
+            table_data.append(row)
+
+        if table_data:
+            cell_text = []
+            for row in table_data:
+                cell_text.append([f'{v:+.1f}' if v is not None else '—' for v in row])
+            tbl = ax2.table(cellText=cell_text, rowLabels=[str(y) for y in years],
+                            colLabels=col_labels, loc='center', cellLoc='center', bbox=[0, 0, 1, 1])
+            tbl.auto_set_font_size(False)
+            tbl.set_fontsize(9)
+            tbl.scale(1, 1.6)
+            # Style header (colLabels row)
+            for j in range(len(col_labels)):
+                tbl[0, j].set_facecolor('#2C3E50')
+                tbl[0, j].set_text_props(color='white', fontweight='bold')
+            # Color cells based on value (sqrt mapping for better visibility of small returns)
+            max_abs = max(abs(v) for row in table_data for v in row if v is not None) or 1
+            for i, row in enumerate(table_data):
+                for j, v in enumerate(row):
+                    if v is not None:
+                        intensity = min((abs(v) / max_abs) ** 0.5, 1.0)
+                        if v >= 0:
+                            r, g, b = 1.0, 1.0 - intensity * 0.55, 1.0 - intensity * 0.55
+                        else:
+                            r, g, b = 1.0 - intensity * 0.55, 1.0, 1.0 - intensity * 0.55
+                        tbl[i + 1, j].set_facecolor((r, g, b))
+                    else:
+                        tbl[i + 1, j].set_facecolor('#f5f5f5')
+                # Annual column: stronger coloring
+                ann_v = row[-1]
+                if ann_v is not None:
+                    intensity = min((abs(ann_v) / max_abs) ** 0.5, 1.0)
+                    if ann_v >= 0:
+                        r, g, b = 1.0, 1.0 - intensity * 0.7, 1.0 - intensity * 0.7
+                    else:
+                        r, g, b = 1.0 - intensity * 0.7, 1.0, 1.0 - intensity * 0.7
+                    tbl[i + 1, len(col_labels) - 1].set_facecolor((r, g, b))
+
+        # Panel 3: NAV curves
+        ax3 = fig.add_subplot(gs[3])
+        ax3.plot(strategy_nav.index, strategy_nav.values,
                  label="轮动策略", linewidth=2, color="#E74C3C")
         if benchmark_nav is not None:
-            ax2.plot(benchmark_nav.index, benchmark_nav.values,
+            ax3.plot(benchmark_nav.index, benchmark_nav.values,
                      label=self.benchmark_name, linewidth=1.5, color="#3498DB", alpha=0.8)
         colors = plt.cm.tab20.colors
         for i, code in enumerate(self.signal_codes):
             if code in asset_navs:
                 cfg = code_config.get(code, {})
                 lbl = cfg.get('name', code) if i < 10 else None
-                ax2.plot(asset_navs[code].index, asset_navs[code].values,
+                ax3.plot(asset_navs[code].index, asset_navs[code].values,
                          label=lbl, linewidth=0.8, alpha=0.4,
                          color=colors[i % len(colors)])
-        ax2.set_title("ETF轮动策略 - 净值曲线", fontsize=16, fontweight="bold")
-        ax2.set_ylabel("净值")
-        ax2.legend(loc="upper left", fontsize=8, ncol=2)
-        ax2.grid(True, alpha=0.3)
-        ax2.set_yscale("log")
-
-        # Panel 3: Drawdown
-        ax3 = fig.add_subplot(gs[3])
-        drawdown = (strategy_nav - s_peak) / s_peak
-        ax3.fill_between(drawdown.index, drawdown.values, 0, alpha=0.5, color="#E74C3C")
-        ax3.set_title("策略回撤", fontsize=12)
-        ax3.set_ylabel("回撤")
+        ax3.set_title("ETF轮动策略 - 净值曲线", fontsize=16, fontweight="bold")
+        ax3.set_ylabel("净值")
+        ax3.legend(loc="upper left", fontsize=8, ncol=2)
         ax3.grid(True, alpha=0.3)
+        ax3.set_yscale("log")
 
-        # Panel 4: Holdings distribution
+        # Panel 4: Drawdown
         ax4 = fig.add_subplot(gs[4])
+        drawdown = (strategy_nav - s_peak) / s_peak
+        ax4.fill_between(drawdown.index, drawdown.values, 0, alpha=0.5, color="#E74C3C")
+        ax4.set_title("策略回撤", fontsize=12)
+        ax4.set_ylabel("回撤")
+        ax4.grid(True, alpha=0.3)
+
+        # Panel 5: Holdings distribution
+        ax5 = fig.add_subplot(gs[5])
         holdings_series = df['holdings']
         for i, code in enumerate(self.signal_codes):
             cfg = code_config.get(code, {})
             name = cfg.get('name', code)
             mask = holdings_series.apply(lambda h: code in h)
             if mask.any():
-                ax4.fill_between(mask.index, i, i + 0.8,
+                ax5.fill_between(mask.index, i, i + 0.8,
                                  where=mask, alpha=0.7,
                                  color=colors[i % len(colors)], label=name)
         ylabels = [code_config.get(c, {}).get('name', c) for c in self.signal_codes]
-        ax4.set_title("每日持仓分布", fontsize=12)
-        ax4.set_yticks(range(len(ylabels)))
-        ax4.set_yticklabels(ylabels, fontsize=7)
-        ax4.grid(True, alpha=0.3)
+        ax5.set_title("每日持仓分布", fontsize=12)
+        ax5.set_yticks(range(len(ylabels)))
+        ax5.set_yticklabels(ylabels, fontsize=7)
+        ax5.grid(True, alpha=0.3)
 
         chart_path = output_dir / 'simple_rotation_report.png'
         plt.savefig(str(chart_path), dpi=150, bbox_inches="tight")