fix(backtest): 结果页垃圾值/无图表端到端修复(empyrical×numpy2.0根因)

根因: empyrical 0.5.5 引用 numpy2.0 已移除的 np.NINF → compute_metrics 静默崩 → _metrics.json 不生成 → 结果页回退 vnpy 原始字段(单位混乱: total_return当百分数、max_drawdown当元 → 前端×100显 3305%/-50M%)。
- metrics.py: 导入 empyrical 前补回 np 别名(NINF/Inf/PINF/NaN/NAN/infty)
- routes.py: benchmark-curve/risk-series 缺 metrics 文件时返空200(不再404拖垮整页); get_result 从 statistics 抽 relative_metrics
- cta_engine.py: bench_df 日期 strip tz 防 pct_change 崩; metrics 块加 traceback 日志
- Result.vue: onMounted 用 Promise.allSettled 隔离7端点, 单接口失败不拖垮整页
- result_store.py: _safe_read_json 容错迁移后残留 NAS 绝对路径, stale path 不崩 list_results
- datareader.py: read_index_daily 改从 vnpy DB 读 + 前缀解析交易所(sh→SSE, 避免 000300 被 guess_exchange 误判 SZSE)

frontend/src/views/backtest/Result.vue

@@ -110,40 +110,75 @@ const filteredBenchmarkCurve = computed(() => filterDataByTimeRange(benchmarkCur
 const filteredRiskSeries = computed(() => filterDataByTimeRange(riskSeries.value))
 
 onMounted(async () => {
-  try {
-    const [info, relMetrics, benchCurve, riskSer, eq, p, tr] = await Promise.all([
-      getResult(taskId),
-      getRelativeMetrics(taskId),
-      getBenchmarkCurve(taskId),
-      getRiskSeries(taskId),
-      getEquityCurve(taskId),
-      getDailyPnl(taskId),
-      getTrades(taskId),
-    ])
+  // 请求隔离：任一接口失败（如 benchmark-curve/risk-series 数据缺失）不得阻塞
+  // 其余请求。statistics/equity/trades 有数据时必须正常渲染。用 Promise.allSettled
+  // 保留并发，逐个取值，失败项保留默认空值 + console.warn。
+  const settled = await Promise.allSettled([
+    getResult(taskId),
+    getRelativeMetrics(taskId),
+    getBenchmarkCurve(taskId),
+    getRiskSeries(taskId),
+    getEquityCurve(taskId),
+    getDailyPnl(taskId),
+    getTrades(taskId),
+  ])
+  const [rInfo, rRel, rBench, rRisk, rEq, rPnl, rTr] = settled
 
-    statistics.value = info.statistics || {}
-    relativeMetrics.value = relMetrics
-    benchmarkCurve.value = benchCurve
-    riskSeries.value = riskSer
-    equity.value = eq
-    pnl.value = p
-    trades.value = tr
-
-    if (info.symbol && info.start && info.end) {
-      try {
-        kline.value = await getKline(info.symbol, info.start, info.end)
-      } catch {
-        kline.value = []
-      }
-    }
-    try {
-      logText.value = await getLog(taskId)
-    } catch {
-      logText.value = ''
-    }
-  } finally {
-    loading.value = false
+  if (rInfo.status === 'fulfilled') {
+    statistics.value = rInfo.value.statistics || {}
+  } else {
+    console.warn('[Result] getResult failed:', rInfo.reason)
   }
+  if (rRel.status === 'fulfilled') {
+    relativeMetrics.value = rRel.value
+  } else {
+    console.warn('[Result] getRelativeMetrics failed:', rRel.reason)
+  }
+  if (rBench.status === 'fulfilled') {
+    benchmarkCurve.value = rBench.value
+  } else {
+    console.warn('[Result] getBenchmarkCurve failed:', rBench.reason)
+  }
+  if (rRisk.status === 'fulfilled') {
+    riskSeries.value = rRisk.value
+  } else {
+    console.warn('[Result] getRiskSeries failed:', rRisk.reason)
+  }
+  if (rEq.status === 'fulfilled') {
+    equity.value = rEq.value
+  } else {
+    console.warn('[Result] getEquityCurve failed:', rEq.reason)
+  }
+  if (rPnl.status === 'fulfilled') {
+    pnl.value = rPnl.value
+  } else {
+    console.warn('[Result] getDailyPnl failed:', rPnl.reason)
+  }
+  if (rTr.status === 'fulfilled') {
+    trades.value = rTr.value
+  } else {
+    console.warn('[Result] getTrades failed:', rTr.reason)
+  }
+
+  // kline 依赖 getResult 返回的 symbol/start/end，单独隔离
+  const info = rInfo.status === 'fulfilled' ? rInfo.value : null
+  if (info?.symbol && info?.start && info?.end) {
+    try {
+      kline.value = await getKline(info.symbol, info.start, info.end)
+    } catch (e) {
+      console.warn('[Result] getKline failed:', e)
+      kline.value = []
+    }
+  }
+
+  try {
+    logText.value = await getLog(taskId)
+  } catch (e) {
+    console.warn('[Result] getLog failed:', e)
+    logText.value = ''
+  }
+
+  loading.value = false
 })
 
 // 每日收益格式化：浮点精度 → 2 位；日期去 00:00:00

sanguo_api/routes.py

@@ -358,7 +358,8 @@ def benchmark_curve(task_id: str):
     """Get benchmark curve data (strategy vs benchmark)."""
     metrics_file = _get_metrics_file_path(task_id)
     if not metrics_file:
-        raise HTTPException(status_code=404, detail="metrics file not found")
+        # metrics 文件缺失时返回空 200（图表优雅降级），不再 404 触发前端整页空白
+        return {"dates": [], "strategy": [], "benchmark": []}
 
     import json
     with open(metrics_file, 'r') as f:
@@ -380,7 +381,8 @@ def risk_series(task_id: str):
     """Get risk series data (alpha, beta, drawdown)."""
     metrics_file = _get_metrics_file_path(task_id)
     if not metrics_file:
-        raise HTTPException(status_code=404, detail="metrics file not found")
+        # metrics 文件缺失时返回空 200（图表优雅降级），不再 404 触发前端整页空白
+        return {"dates": [], "alpha": [], "beta": [], "drawdown": [], "strategy_vol": [], "benchmark_vol": []}
 
     import json
     with open(metrics_file, 'r') as f:

sanguo_backtest/cta_engine.py

@@ -254,6 +254,11 @@ def run_cta_backtest(
                 if bench_df is not None and not bench_df.empty and "close" in bench_df.columns:
                     # Calculate benchmark daily returns
                     bench_df["date"] = pd.to_datetime(bench_df["date"])
+                    # 去时区：daily_df.index 是 tz-naive，benchmark 若 tz-aware 会让
+                    # compute_metrics 内部 reindex 抛 TypeError（被上层 except 静默吞掉，
+                    # 致 _metrics.json 不生成）。统一去掉 tz 保证对齐。
+                    if getattr(bench_df["date"].dt, "tz", None) is not None:
+                        bench_df["date"] = bench_df["date"].dt.tz_localize(None)
                     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:])
@@ -285,8 +290,12 @@ def run_cta_backtest(
                         json.dump({"series": series_data}, f, indent=2)
 
             except Exception as metrics_error:
-                # Log but don't fail backtest if metrics calculation fails
-                logging.warning("相对指标计算失败（回测结果不受影响）: %s", metrics_error)
+                # Log but don't fail backtest if metrics calculation fails.
+                # 附 traceback 以便定位（_metrics.json 不生成时这里是根因）。
+                logging.warning(
+                    "相对指标计算失败（回测结果不受影响）: %s\n%s",
+                    metrics_error, traceback.format_exc(),
+                )
 
         # Build equity curve DataFrame (S1.2): use the daily_df returned by
         # calculate_result (index=date, has a 'balance' column). get_all_daily_results

sanguo_backtest/metrics.py

@@ -3,6 +3,13 @@ from dataclasses import dataclass, field
 from typing import Dict, Literal
 import math
 import numpy as np
+# empyrical 0.5.5 引用了 NumPy 2.0 已移除的别名（np.NINF / np.NaN / np.Inf / np.PINF），
+# 不补回会在 sortino_ratio/downside_risk 等函数里抛 AttributeError，导致整块相对指标计算
+# 失败、_metrics.json 不生成、结果页回退到 vnpy 原始字段（单位混乱）。
+for _alias, _val in (("NINF", -np.inf), ("Inf", np.inf), ("PINF", np.inf),
+                     ("NaN", np.nan), ("NAN", np.nan), ("infty", np.inf)):
+    if not hasattr(np, _alias):
+        setattr(np, _alias, _val)
 import pandas as pd
 import empyrical
 

sanguo_backtest/result_store.py

@@ -1,11 +1,14 @@
 """Backtest result storage using SQLite + parquet files."""
 import sqlite3
 import json
+import logging
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Optional
 import pandas as pd
 
+logger = logging.getLogger(__name__)
+
 
 @dataclass
 class BacktestResult:
@@ -109,6 +112,22 @@ def save_result(result: BacktestResult, db_path: str, file_dir: Optional[str] =
         conn.close()
 
 
+def _safe_read_json(path: Optional[str]) -> Optional[pd.DataFrame]:
+    """Read a JSON equity/trades file; return None if missing or unreadable.
+
+    Historical records may reference paths from a previous host (e.g. NAS
+    absolute paths after migration to VPS). Swallow those failures so the
+    record stays listable instead of crashing list_results.
+    """
+    if not path:
+        return None
+    try:
+        return pd.read_json(path, orient="records")
+    except (FileNotFoundError, ValueError, OSError) as e:
+        logger.warning("result_store: skipping unreadable JSON file %s: %s", path, e)
+        return None
+
+
 def load_result(rid: int, db_path: str) -> BacktestResult:
     """
     Load backtest result by ID from database.
@@ -133,9 +152,11 @@ def load_result(rid: int, db_path: str) -> BacktestResult:
         cols = [d[0] for d in conn.execute("SELECT * FROM backtest_stats LIMIT 0").description]
         d = dict(zip(cols, row))
 
-        # Load JSON files if paths exist (equity_curve/trades persisted as JSON)
-        equity = pd.read_json(d["equity_path"], orient="records") if d.get("equity_path") else None
-        trades = pd.read_json(d["trades_path"], orient="records") if d.get("trades_path") else None
+        # Load JSON files if paths exist (equity_curve/trades persisted as JSON).
+        # Tolerate stale paths (e.g. NAS absolute paths left after VPS migration):
+        # missing/unreadable file -> None, so record still appears in list_results.
+        equity = _safe_read_json(d.get("equity_path"))
+        trades = _safe_read_json(d.get("trades_path"))
 
         return BacktestResult(
             task_id=d["task_id"],

sanguo_data/datareader.py

@@ -105,41 +105,60 @@ def read_parquet_15min(symbol: str, start: str, end: str, cfg, dir_key: str = "m
     return bars
 
 
-def read_index_daily(code: str, start: date, end: date, cfg) -> pd.DataFrame:
+def read_index_daily(code: str, start, end, cfg) -> pd.DataFrame:
     """
-    读指数日线数据（sh000300/sz000905），复用 read_parquet_daily 的年分片 parquet 路径
+    读指数日线数据（sh000300/sz399001 等），从 vnpy DB 读取（统一数据源）。
+    parquet 仅作原始备份，不再读取。返回类型保持 pd.DataFrame（cta_engine 消费不变）。
 
     Args:
-        code: 指数代码，如 "sh000300"（沪深300）、"sz000905"（中证500）
-        start: 起始日期
-        end: 结束日期
+        code: 指数代码，带交易所前缀，如 "sh000300"（沪深300）、"sz399001"（深证成指）
+        start: 起始日期（str "YYYY-MM-DD" / date / datetime）
+        end: 结束日期（str "YYYY-MM-DD" / date / datetime）
         cfg: 数据配置对象
 
     Returns:
-        pd.DataFrame: 包含 date/open/high/low/close/volume 列的日线数据
+        pd.DataFrame: date/open/high/low/close/volume 列；无数据返回空 DataFrame。
     """
-    daily_dir = Path(cfg.data_paths["daily_dir"])
-    start_dt = start if isinstance(start, datetime) else datetime.combine(start, datetime.min.time())
-    end_dt = end if isinstance(end, datetime) else datetime.combine(end, datetime.max.time())
+    from vnpy.trader.database import get_database  # lazy：避免模块 import 依赖数据库驱动
 
-    dfs: list[pd.DataFrame] = []
+    # 前缀解析交易所（指数不能用 guess_exchange：000300 以 0 开头会被误判成 SZSE，
+    # 但 000300 实际属于 SSE）。sh → SSE，sz → SZSE。
+    symbol = code[2:]
+    exchange = Exchange.SSE if code.startswith("sh") else Exchange.SZSE
 
-    # 按年分片读取（与 read_parquet_daily 相同路径逻辑）
-    for year in range(start_dt.year, end_dt.year + 1):
-        f = daily_dir / str(year) / f"{code}_daily.parquet"
-        if not f.exists():
-            continue
-        df = pd.read_parquet(f)
-        # 过滤日期范围
-        df["date"] = pd.to_datetime(df["date"])
-        mask = (df["date"] >= start_dt) & (df["date"] <= end_dt)
-        filtered_df = df[mask].copy()
-        if not filtered_df.empty:
-            dfs.append(filtered_df)
+    # 日期归一化：str → parse, date → combine, datetime → as-is
+    def _to_dt(s, is_start: bool) -> datetime:
+        if isinstance(s, datetime):
+            return s
+        if isinstance(s, date):
+            return datetime.combine(s, datetime.min.time() if is_start else datetime.max.time())
+        return datetime.strptime(s, "%Y-%m-%d")
 
-    if dfs:
-        result = pd.concat(dfs, ignore_index=True)
-        result = result.sort_values("date")
-        return result.reset_index(drop=True)
-    else:
+    start_dt = _to_dt(start, True)
+    end_dt = _to_dt(end, False)
+
+    # 配置 vnpy DB（与 read_db_daily 同模式）
+    SETTINGS["database.name"] = "sqlite"
+    SETTINGS["database.database"] = cfg.data_paths["vnpy_db"]
+
+    db = get_database()
+    bars = db.load_bar_data(
+        symbol=symbol,
+        exchange=exchange,
+        interval=Interval.DAILY,
+        start=start_dt,
+        end=end_dt,
+    )
+
+    if not bars:
         return pd.DataFrame(columns=["date", "open", "high", "low", "close", "volume"])
+
+    df = pd.DataFrame([{
+        "date": b.datetime,
+        "open": b.open_price,
+        "high": b.high_price,
+        "low": b.low_price,
+        "close": b.close_price,
+        "volume": b.volume,
+    } for b in bars])
+    return df.sort_values("date").reset_index(drop=True)