fix(backtest): NaN/Inf浮点→None消毒(vnpy统计+empyrical指标+时序)修JSON序列化500

sanguo_backtest/cta_engine.py

@@ -1,6 +1,7 @@
 """CTA strategy backtesting engine wrapper using vnpy_ctastrategy.backtesting."""
 import sys
 import os
+import math
 import traceback
 import uuid
 from datetime import datetime
@@ -120,9 +121,11 @@ def run_cta_backtest(strategy_class, symbol: str, params: dict, start: str, end:
         # calculate_statistics(df) returns the stats dict (sharpe/drawdown/etc.)
         daily_df = engine.calculate_result()
         raw_stats = engine.calculate_statistics(daily_df, output=False) or {}
-        # Ensure JSON-serializable (vnpy may include Timestamp / non-numeric values)
+        # Ensure JSON-serializable (vnpy may include Timestamp / non-numeric / NaN values)
         statistics = {
-            k: (v if isinstance(v, (int, float, str, bool)) or v is None else str(v))
+            k: (None if (isinstance(v, float) and not math.isfinite(v))
+                else v if isinstance(v, (int, float, str, bool)) or v is None
+                else str(v))
             for k, v in raw_stats.items()
         }
 
@@ -173,7 +176,8 @@ def run_cta_backtest(strategy_class, symbol: str, params: dict, start: str, end:
                         if isinstance(series, pd.Series):
                             series_data[key] = {
                                 "dates": series.index.astype(str).tolist(),
-                                "values": series.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

sanguo_backtest/metrics.py

@@ -1,6 +1,7 @@
 """回测相对/绝对指标计算（empyrical，聚宽同源口径）。纯函数。"""
 from dataclasses import dataclass, field
 from typing import Dict, Literal
+import math
 import numpy as np
 import pandas as pd
 import empyrical
@@ -44,6 +45,8 @@ def compute_metrics(
         "benchmark_return": float(empyrical.cum_returns_final(b)),
         "benchmark_volatility": float(empyrical.annual_volatility(b, period='daily')),
     }
+    # Sanitize non-finite floats (NaN/Inf from degenerate inputs) → None for JSON safety
+    scalars = {k: (None if isinstance(v, float) and not math.isfinite(v) else v) for k, v in scalars.items()}
 
     equity = empyrical.cum_returns(s)
     bench_curve = empyrical.cum_returns(b)