fix(backtest): CTA metrics benchmark 缺失降级(不阻塞整组指标图)

benchmark 数据缺失时原实现 if 跳过整段 compute_metrics → {task_id}_metrics.json 不写 → 策略净值/回撤/波动图也空(不只基准图)。改为:benchmark 空时传空 Series + log WARN,compute_metrics 内部 reindex→fillna(0) 容空(基准类指标 NaN→None,策略指标正常),metrics.json 照写。效果:策略图照常出,只基准/Alpha/Beta 图空(前端 benchmark-curve/risk-series 已容错)。加测试 test_run_cta_backtest_benchmark_missing_degrades(mock read_index_daily 返空 → 验 compute_metrics 仍 called + metrics.json 仍写 + 策略指标有值)。6 测试全绿(Mac pytest)。

sanguo_backtest/cta_engine.py

@@ -317,32 +317,42 @@ def run_cta_backtest(
                     bench_df = bench_df.sort_values("date")
                     benchmark_returns = bench_df["close"].pct_change().dropna()
                     benchmark_returns.index = pd.to_datetime(bench_df["date"].iloc[1:])
+                else:
+                    # 基准缺失：降级。compute_metrics 内部 reindex→ffill→fillna(0) 能容空
+                    # benchmark(基准类指标返 NaN→None,策略指标正常算),故仍执行 + metrics.json
+                    # 照写。避免原实现整段跳过 → 策略净值/回撤/波动图也空(不只基准图)。
+                    benchmark_returns = pd.Series(dtype=float)
+                    logging.warning(
+                        "基准数据缺失,降级计算(基准/Alpha/Beta 图将空,策略指标不受影响): "
+                        "benchmark=%s %s~%s",
+                        benchmark_code, start_date.date(), end_date.date(),
+                    )
 
-                    # H4: compute_metrics 内部从 daily_df["balance"] 自算 simple return，
-                    # 不再依赖 vnpy 的 log return 列（删原三路 fallback）
+                # H4: compute_metrics 内部从 daily_df["balance"] 自算 simple return，
+                # 不再依赖 vnpy 的 log return 列（删原三路 fallback）
 
-                    # Compute relative metrics
-                    metrics_result = compute_metrics(daily_df, benchmark_returns)
+                # Compute relative metrics（benchmark 有无都执行；空时基准类指标 NaN→None）
+                metrics_result = compute_metrics(daily_df, benchmark_returns)
 
-                    # Merge scalars into statistics (for API response)
-                    statistics.update(metrics_result.scalars)
+                # Merge scalars into statistics (for API response)
+                statistics.update(metrics_result.scalars)
 
-                    # Serialize series to JSON (separate file, same as equity_curve/trades)
-                    import json
-                    series_data = {}
-                    for key, series in metrics_result.series.items():
-                        if isinstance(series, pd.Series):
-                            series_data[key] = {
-                                "dates": series.index.astype(str).tolist(),
-                                "values": [None if (isinstance(x, float) and not math.isfinite(x)) else x
-                                           for x in series.tolist()]
-                            }
+                # Serialize series to JSON (separate file, same as equity_curve/trades)
+                import json
+                series_data = {}
+                for key, series in metrics_result.series.items():
+                    if isinstance(series, pd.Series):
+                        series_data[key] = {
+                            "dates": series.index.astype(str).tolist(),
+                            "values": [None if (isinstance(x, float) and not math.isfinite(x)) else x
+                                       for x in series.tolist()]
+                        }
 
-                    # Write metrics series to JSON file
-                    file_dir = os.path.dirname(os.path.abspath(db_path))
-                    metrics_file = os.path.join(file_dir, f"{task_id}_metrics.json")
-                    with open(metrics_file, "w") as f:
-                        json.dump({"series": series_data}, f, indent=2)
+                # Write metrics series to JSON file
+                file_dir = os.path.dirname(os.path.abspath(db_path))
+                metrics_file = os.path.join(file_dir, f"{task_id}_metrics.json")
+                with open(metrics_file, "w") as f:
+                    json.dump({"series": series_data}, f, indent=2)
 
             except Exception as metrics_error:
                 # Log but don't fail backtest if metrics calculation fails.

tests/backtest/test_cta_engine.py

@@ -360,4 +360,87 @@ class TestRunCtaBacktest:
         # Verify read_index_daily was called with hs300 code (sh000300)
         mock_read.assert_called_once()
         call_args = mock_read.call_args
-        assert call_args[0][0] == "sh000300", "Default benchmark should be hs300 (sh000300)"
+        assert call_args[0][0] == "sh000300", "Default benchmark should be hs300 (sh000300)"
+
+    def test_run_cta_backtest_benchmark_missing_degrades(self, temp_db_path):
+        """benchmark 缺失时 compute_metrics 仍执行、metrics.json 照写(降级)。
+
+        根因:read_index_daily 返空时原实现 if 跳过整段 compute_metrics →
+        {task_id}_metrics.json 不写 → 策略净值/回撤/波动图也空(不只基准图)。
+        降级:benchmark 空时传空 Series 给 compute_metrics(内部 reindex→fillna(0),
+        基准类指标返 NaN→None,策略指标正常),metrics.json 照写。
+        """
+        mock_strategy_class = Mock()
+        mock_strategy_class.__name__ = "DegBmStrategy"
+
+        dates = pd.date_range("2024-01-01", "2024-03-31", freq="D")
+        daily_df = pd.DataFrame(
+            {"balance": [1_000_000.0 * (1.001 ** i) for i in range(len(dates))]},
+            index=dates,
+        )
+
+        mock_engine = MagicMock()
+        mock_engine.calculate_result.return_value = daily_df
+        mock_engine.calculate_statistics.return_value = {"total_return": 0.15}
+        mock_engine.trades = {"t1": MagicMock()}  # 非空 trades 避免 degenerate
+        mock_engine.history_data = []  # 空 history → 定寸跳过
+
+        mock_cfg = Mock()
+        mock_cfg.data_paths = {"daily_dir": "/mock/daily_dir"}
+
+        mock_ashare = MagicMock()
+        mock_ashare.AShareBacktestingEngine = Mock(return_value=mock_engine)
+
+        mock_metrics_result = Mock()
+        mock_metrics_result.scalars = {
+            "total_return": 0.15,       # 策略指标(不依赖基准)有值
+            "max_drawdown": -0.05,
+            "alpha": None,              # 基准类指标 NaN→None(基准缺失)
+            "benchmark_return": None,
+        }
+        mock_metrics_result.series = {
+            "equity_curve": pd.Series([1.0, 1.05, 1.1]),
+            "drawdown": pd.Series([0.0, -0.01, -0.02]),
+        }
+
+        mock_tzlocal = MagicMock()
+        mock_tzlocal.get_localzone_name = Mock(return_value="UTC")
+
+        with patch.dict("sys.modules", {
+            "sanguo_backtest.ashare_engine": mock_ashare,
+            "tzlocal": mock_tzlocal,
+            "vnpy.trader.setting": MagicMock(),
+        }):
+            # read_index_daily 返空 DataFrame = benchmark 缺失
+            with patch("sanguo_backtest.cta_engine.read_index_daily", return_value=pd.DataFrame()):
+                with patch("sanguo_backtest.cta_engine.compute_metrics", return_value=mock_metrics_result) as mock_cm:
+                    result = run_cta_backtest(
+                        strategy_class=mock_strategy_class,
+                        symbol="600000",
+                        params={},
+                        start="2024-01-01",
+                        end="2024-03-31",
+                        cfg=mock_cfg,
+                        db_path=temp_db_path,
+                    )
+
+        # 1. compute_metrics 仍被调用(降级,没因 benchmark 空跳过)
+        mock_cm.assert_called_once()
+        # 2. 传入的 benchmark_returns 是空 Series(降级标记)
+        benchmark_arg = mock_cm.call_args[0][1]
+        assert isinstance(benchmark_arg, pd.Series)
+        assert benchmark_arg.empty, "benchmark 缺失时应传空 Series 给 compute_metrics"
+
+        # 3. metrics.json 仍写(策略净值/回撤/波动图数据源)
+        file_dir = Path(temp_db_path).parent
+        metrics_file = file_dir / f"{result.task_id}_metrics.json"
+        assert metrics_file.exists(), f"降级时 metrics.json 应照写: {metrics_file}"
+        with open(metrics_file) as f:
+            metrics_data = json.load(f)
+        series_keys = list(metrics_data.get("series", {}).keys())
+        assert "equity_curve" in series_keys
+        assert "drawdown" in series_keys
+
+        # 4. 策略指标有值(基准类 None)
+        assert result.statistics.get("total_return") == 0.15
+        assert result.statistics.get("alpha") is None